Biomarkers and methods for predicting preterm birth

ABSTRACT

The disclosure provides biomarker panels, methods and kits for determining the probability for preterm birth in a pregnant female. The present disclosure is based, in part, on the discovery that certain proteins and peptides in biological samples obtained from a pregnant female are differentially expressed in pregnant females that have an increased risk of developing in the future or presently suffering from preterm birth relative to matched controls. The present disclosure is further based, in part, on the unexpected discovery that panels combining one or more of these proteins and peptides can be utilized in methods of determining the probability for preterm birth in a pregnant female with relatively high sensitivity and specificity. These proteins and peptides disclosed herein serve as biomarkers for classifying test samples, predicting a probability of preterm birth, monitoring of progress of preterm birth in a pregnant female, either individually or in a panel of biomarkers.

This application is a continuation of application Ser. No. 15/286,486, filed Oct. 5, 2016, which is a continuation of application Ser. No. 14/213,861, filed Mar. 14, 2014, which claims the benefit of U.S. provisional patent application No. 61/919,586, filed Dec. 20, 2013, and U.S. provisional application No. 61/798,504, filed Mar. 15, 2013, each of which is incorporated herein by reference in its entirety.

This application incorporates by reference a Sequence Listing submitted herewith as an ASCII text file entitled 13271-033-999_SL.txt created on Jan. 15, 2019, and having a size of 216,359 bytes.

The invention relates generally to the field of personalized medicine and, more specifically to compositions and methods for determining the probability for preterm birth in a pregnant female.

BACKGROUND

According to the World Heath Organization, an estimated 15 million babies are born preterm (before 37 completed weeks of gestation) every year. In almost all countries with reliable data, preterm birth rates are increasing. See, World Health Organization; March of Dimes; The Partnership for Maternal, Newborn & Child Health; Save the Children, Born too soon: the global action report on preterm birth, ISBN 9789241503433(2012). An estimated 1 million babies die annually from preterm birth complications. Globally, preterm birth is the leading cause of newborn deaths (babies in the first four weeks of life) and the second leading cause of death after pneumonia in children under five years. Many survivors face a lifetime of disability, including learning disabilities and visual and hearing problems.

Across 184 countries with reliable data, the rate of preterm birth ranges from 5% to 18% of babies born. Blencowe et al., “National, regional and worldwide estimates of preterm birth.” The Lancet, 9; 379(9832):2162-72 (2012). While over 60% of preterm births occur in Africa and south Asia, preterm birth is nevertheless a global problem. Countries with the highest numbers include Brazil, India, Nigeria and the United States of America. Of the 11 countries with preterm birth rates over 15%, all but two are in sub-Saharan Africa. In the poorest countries, on average, 12% of babies are born too soon compared with 9% in higher-income countries. Within countries, poorer families are at higher risk. More than three-quarters of premature babies can be saved with feasible, cost-effective care, for example, antenatal steroid injections given to pregnant women at risk of preterm labour to strengthen the babies' lungs.

Infants born preterm are at greater risk than infants born at term for mortality and a variety of health and developmental problems. Complications include acute respiratory, gastrointestinal, immunologic, central nervous system, hearing, and vision problems, as well as longer-term motor, cognitive, visual, hearing, behavioral, social-emotional, health, and growth problems. The birth of a preterm infant can also bring considerable emotional and economic costs to families and have implications for public-sector services, such as health insurance, educational, and other social support systems. The greatest risk of mortality and morbidity is for those infants born at the earliest gestational ages. However, those infants born nearer to term represent the greatest number of infants born preterm and also experience more complications than infants born at term.

To prevent preterm birth in women who are less than 24 weeks pregnant with an ultrasound showing cervical opening, a surgical procedure known as cervical cerclage can be employed in which the cervix is stitched closed with strong sutures. For women less than 34 weeks pregnant and in active preterm labor, hospitalization may be necessary as well as the administration of medications to temporarily halt preterm labor an/or promote the fetal lung development. If a pregnant women is determined to be at risk for preterm birth, health care providers can implement various clinical strategies that may include preventive medications, for example, hydroxyprogesterone caproate (Makena) injections and/or vaginal progesterone gel, cervical pessaries, restrictions on sexual activity and/or other physical activities, and alterations of treatments for chronic conditions, such as diabetes and high blood pressure, that increase the risk of preterm labor.

There is a great need to identify and provide women at risk for preterm birth with proper antenatal care. Women identified as high-risk can be scheduled for more intensive antenatal surveillance and prophylactic interventions. Current strategies for risk assessment are based on the obstetric and medical history and clinical examination, but these strategies are only able to identify a small percentage of women who are at risk for preterm delivery. Reliable early identification of risk for preterm birth would enable planning appropriate monitoring and clinical management to prevent preterm delivery. Such monitoring and management might include: more frequent prenatal care visits, serial cervical length measurements, enhanced education regarding signs and symptoms of early preterm labor, lifestyle interventions for modifiable risk behaviors, cervical pessaries and progesterone treatment. Finally, reliable antenatal identification of risk for preterm birth also is crucial to cost-effective allocation of monitoring resources.

The present invention addresses this need by providing compositions and methods for determining whether a pregnant woman is at risk for preterm birth. Related advantages are provided as well.

SUMMARY

The present invention provides compositions and methods for predicting the probability of preterm birth in a pregnant female.

In one aspect, the invention provides a panel of isolated biomarkers comprising N of the biomarkers listed in Tables 1 through 63. In some embodiments, N is a number selected from the group consisting of 2 to 24. In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of AFTECCVVASQLR, ELLESYIDGR, and ITLPDFTGDLR. In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of FLNWIK, FGFGGSTDSGPIR, LLELTGPK, VEHSDLSFSK, IEGNLIFDPNNYLPK, ALVLELAK, TQILEWAAER, DVLLLVHNLPQNLPGYFWYK, SEPRPGVLLR, ITQDAQLK, ALDLSLK, WWGGQPLWITATK, and LSETNR

In further embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of the biomarkers set forth in Table 50 and the biomarkers set forth in Table 52.

In a further aspect, the invention provides a panel of isolated biomarkers comprising N of the biomarkers listed in Tables 1 through 63. In some embodiments, N is a number selected from the group consisting of 2 to 24. In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of the biomarkers set forth in Table 50 and the biomarkers set forth in Table 52.

In some embodiments, the invention provides a biomarker panel comprising at least two of the isolated biomarkers selected from the group consisting of lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), and complement component C8 gamma chain (C8G or CO8G).

In some embodiments, the invention provides a biomarker panel comprising at least two of the isolated biomarkers selected from the group consisting of Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

In other embodiments, the invention provides a biomarker panel comprising lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), complement component C8 gamma chain (C8G or CO8G), complement component 1, q subcomponent, B chain (C1QB), fibrinogen beta chain (FIBB or FIB), C-reactive protein (CRP), inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), chorionic somatomammotropin hormone (CSH), and angiotensinogen (ANG or ANGT).

In other embodiments, the invention provides a biomarker panel comprising Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

In additional embodiments, the invention provides a biomarker panel comprising at least two of the isolated biomarkers selected from the group consisting of the biomarkers set forth in Table 51 and the biomarkers set forth in Table 53.

Also provided by the invention is a method of determining probability for preterm birth in a pregnant female comprising detecting a measurable feature of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63 in a biological sample obtained from the pregnant female, and analyzing the measurable feature to determine the probability for preterm birth in the pregnant female. In some embodiments, the invention provides a method of predicting GAB, the method encompassing detecting a measurable feature of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63 in a biological sample obtained from a pregnant female, and analyzing said measurable feature to predict GAB.

In some embodiments, a measurable feature comprises fragments or derivatives of each of the N biomarkers selected from the biomarkers listed in Tables 1 through 63. In some embodiments of the disclosed methods detecting a measurable feature comprises quantifying an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63, combinations or portions and/or derivatives thereof in a biological sample obtained from the pregnant female. In additional embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female further encompass detecting a measurable feature for one or more risk indicia associated with preterm birth.

In some embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of N biomarkers, wherein N is selected from the group consisting of 2 to 24. In further embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of AFTECCVVASQLR, ELLESYIDGR, and ITLPDFTGDLR. In further embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of FLNWIK, FGFGGSTDSGPIR, LLELTGPK, VEHSDLSFSK, IEGNLIFDPNNYLPK, ALVLELAK, TQILEWAAER, DVLLLVHNLPQNLPGYFWYK, SEPRPGVLLR, ITQDAQLK, ALDLSLK, WWGGQPLWITATK, and LSETNR. In further embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of the biomarkers set forth in Table 50 and the biomarkers set forth in Table 52.

In other embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), and complement component C8 gamma chain (C8G or CO8G).

In other embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

In further embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), complement component C8 gamma chain (C8G or CO8G), complement component 1, q subcomponent, B chain (C1QB), fibrinogen beta chain (FIBB or FIB), C-reactive protein (CRP), inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), chorionic somatomammotropin hormone (CSH), and angiotensinogen (ANG or ANGT).

In further embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of the biomarkers set forth in Table 51 and the biomarkers set forth in Table 53.

In some embodiments of the methods of determining probability for preterm birth in a pregnant female, the probability for preterm birth in the pregnant female is calculated based on the quantified amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63. In some embodiments, the disclosed methods for determining the probability of preterm birth encompass detecting and/or quantifying one or more biomarkers using mass spectrometry, a capture agent or a combination thereof.

In some embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female encompass an initial step of providing a biomarker panel comprising N of the biomarkers listed in Tables 1 through 63. In additional embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female encompass an initial step of providing a biological sample from the pregnant female.

In some embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female encompass communicating the probability to a health care provider. In additional embodiments, the communication informs a subsequent treatment decision for the pregnant female. In further embodiments, the treatment decision of one or more selected from the group of consisting of more frequent prenatal care visits, serial cervical length measurements, enhanced education regarding signs and symptoms of early preterm labor, lifestyle interventions for modifiable risk behaviors and progesterone treatment.

In further embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female encompass analyzing the measurable feature of one or more isolated biomarkers using a predictive model. In some embodiments of the disclosed methods, a measurable feature of one or more isolated biomarkers is compared with a reference feature.

In additional embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female encompass using one or more analyses selected from a linear discriminant analysis model, a support vector machine classification algorithm, a recursive feature elimination model, a prediction analysis of microarray model, a logistic regression model, a CART algorithm, a flex tree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, a machine learning algorithm, a penalized regression method, and a combination thereof. In one embodiment, the disclosed methods of determining probability for preterm birth in a pregnant female encompass logistic regression.

In some embodiments, the invention provides a method of determining probability for preterm birth in a pregnant female, the method encompassing quantifying in a biological sample obtained from the pregnant female an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63; multiplying the amount by a predetermined coefficient, and determining the probability for preterm birth in the pregnant female comprising adding the individual products to obtain a total risk score that corresponds to the probability

In additional embodiments, the invention provides a method of predicting GAB, the method comprising: (a) quantifying in a biological sample obtained from said pregnant female an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63; (b) multiplying or thresholding said amount by a predetermined coefficient, (c) determining the predicted GAB birth in said pregnant female comprising adding said individual products to obtain a total risk score that corresponds to said predicted GAB.

In further embodiments, the invention provides a method of predicting time to birth in a pregnant female, the method comprising: (a) obtaining a biological sample from said pregnant female; (b) quantifying an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63 in said biological sample; (c) multiplying or thresholding said amount by a predetermined coefficient, (d) determining predicted GAB in said pregnant female comprising adding said individual products to obtain a total risk score that corresponds to said predicted GAB; and (e) subtracting the estimated gestational age (GA) at time biological sample was obtained from the predicted GAB to predict time to birth in said pregnant female.

Other features and advantages of the invention will be apparent from the detailed description, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1. Scatterplot of actual gestational age at birth versus predicted gestational age from random forest regression model.

FIG. 2. Distribution of predicted gestational age from random forest regression model versus actual gestational age at birth (GAB), where actual GAB is given in categories of (i) less than 37 weeks, (ii) 37 to 39 weeks, and (iii) 40 weeks or greater (peaks left to right, respectively).

DETAILED DESCRIPTION

The present disclosure is based, in part, on the discovery that certain proteins and peptides in biological samples obtained from a pregnant female are differentially expressed in pregnant females that have an increased risk of preterm birth relative to controls. The present disclosure is further based, in part, on the unexpected discovery that panels combining one or more of these proteins and peptides can be utilized in methods of determining the probability for preterm birth in a pregnant female with high sensitivity and specificity. These proteins and peptides disclosed herein serve as biomarkers for classifying test samples, predicting probability of preterm birth, predicting probability of term birth, predicting gestational age at birth (GAB), predicting time to birth and/or monitoring of progress of preventative therapy in a pregnant female, either individually or in a panel of biomarkers.

The disclosure provides biomarker panels, methods and kits for determining the probability for preterm birth in a pregnant female. One major advantage of the present disclosure is that risk of developing preterm birth can be assessed early during pregnancy so that appropriate monitoring and clinical management to prevent preterm delivery can be initiated in a timely fashion. The present invention is of particular benefit to females lacking any risk factors for preterm birth and who would not otherwise be identified and treated.

By way of example, the present disclosure includes methods for generating a result useful in determining probability for preterm birth in a pregnant female by obtaining a dataset associated with a sample, where the dataset at least includes quantitative data about biomarkers and panels of biomarkers that have been identified as predictive of preterm birth, and inputting the dataset into an analytic process that uses the dataset to generate a result useful in determining probability for preterm birth in a pregnant female. As described further below, this quantitative data can include amino acids, peptides, polypeptides, proteins, nucleotides, nucleic acids, nucleosides, sugars, fatty acids, steroids, metabolites, carbohydrates, lipids, hormones, antibodies, regions of interest that serve as surrogates for biological macromolecules and combinations thereof.

In addition to the specific biomarkers identified in this disclosure, for example, by accession number in a public database, sequence, or reference, the invention also contemplates use of biomarker variants that are at least 90% or at least 95% or at least 97% identical to the exemplified sequences and that are now known or later discovered and that have utility for the methods of the invention. These variants may represent polymorphisms, splice variants, mutations, and the like. In this regard, the instant specification discloses multiple art-known proteins in the context of the invention and provides exemplary accession numbers associated with one or more public databases as well as exemplary references to published journal articles relating to these art-known proteins. However, those skilled in the art appreciate that additional accession numbers and journal articles can easily be identified that can provide additional characteristics of the disclosed biomarkers and that the exemplified references are in no way limiting with regard to the disclosed biomarkers. As described herein, various techniques and reagents find use in the methods of the present invention. Suitable samples in the context of the present invention include, for example, blood, plasma, serum, amniotic fluid, vaginal secretions, saliva, and urine. In some embodiments, the biological sample is selected from the group consisting of whole blood, plasma, and serum. In a particular embodiment, the biological sample is serum. As described herein, biomarkers can be detected through a variety of assays and techniques known in the art. As further described herein, such assays include, without limitation, mass spectrometry (MS)-based assays, antibody-based assays as well as assays that combine aspects of the two.

Protein biomarkers associated with the probability for preterm birth in a pregnant female include, but are not limited to, one or more of the isolated biomarkers listed in Tables 1 through 63. In addition to the specific biomarkers, the disclosure further includes biomarker variants that are about 90%, about 95%, or about 97% identical to the exemplified sequences. Variants, as used herein, include polymorphisms, splice variants, mutations, and the like.

Additional markers can be selected from one or more risk indicia, including but not limited to, maternal characteristics, medical history, past pregnancy history, and obstetrical history. Such additional markers can include, for example, previous low birth weight or preterm delivery, multiple 2nd trimester spontaneous abortions, prior first trimester induced abortion, familial and intergenerational factors, history of infertility, nulliparity, placental abnormalities, cervical and uterine anomalies, short cervical length measurements, gestational bleeding, intrauterine growth restriction, in utero diethylstilbestrol exposure, multiple gestations, infant sex, short stature, low prepregnancy weight, low or high body mass index, diabetes, hypertension, urogenital infections (i.e. urinary tract infection), asthma, anxiety and depression, asthma, hypertension, hypothyroidism. Demographic risk indicia for preterm birth can include, for example, maternal age, race/ethnicity, single marital status, low socioeconomic status, maternal age, employment-related physical activity, occupational exposures and environment exposures and stress. Further risk indicia can include, inadequate prenatal care, cigarette smoking, use of marijuana and other illicit drugs, cocaine use, alcohol consumption, caffeine intake, maternal weight gain, dietary intake, sexual activity during late pregnancy and leisure-time physical activities. (Preterm Birth: Causes, Consequences, and Prevention, Institute of Medicine (US) Committee on Understanding Premature Birth and Assuring Healthy Outcomes; Behrman R E, Butler A S, editors. Washington (DC): National Academies Press (US); 2007). Additional risk indicia useful for as markers can be identified using learning algorithms known in the art, such as linear discriminant analysis, support vector machine classification, recursive feature elimination, prediction analysis of microarray, logistic regression, CART, FlexTree, LART, random forest, MART, and/or survival analysis regression, which are known to those of skill in the art and are further described herein.

Provided herein are panels of isolated biomarkers comprising N of the biomarkers selected from the group listed in Tables 1 through 63. In the disclosed panels of biomarkers N can be a number selected from the group consisting of 2 to 24. In the disclosed methods, the number of biomarkers that are detected and whose levels are determined, can be 1, or more than 1, such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 or more. In certain embodiments, the number of biomarkers that are detected, and whose levels are determined, can be 1, or more than 1, such as 2, 3, 4, 5, 6, 7, 8, 9, 10, or more. The methods of this disclosure are useful for determining the probability for preterm birth in a pregnant female.

While certain of the biomarkers listed in Tables 1 through 63 are useful alone for determining the probability for preterm birth in a pregnant female, methods are also described herein for the grouping of multiple subsets of the biomarkers that are each useful as a panel of three or more biomarkers. In some embodiments, the invention provides panels comprising N biomarkers, wherein N is at least three biomarkers. In other embodiments, N is selected to be any number from 3-23 biomarkers.

In yet other embodiments, N is selected to be any number from 2-5, 2-10, 2-15, 2-20, or 2-23. In other embodiments, N is selected to be any number from 3-5, 3-10, 3-15, 3-20, or 3-23. In other embodiments, N is selected to be any number from 4-5, 4-10, 4-15, 4-20, or 4-23. In other embodiments, N is selected to be any number from 5-10, 5-15, 5-20, or 5-23. In other embodiments, N is selected to be any number from 6-10, 6-15, 6-20, or 6-23. In other embodiments, N is selected to be any number from 7-10, 7-15, 7-20, or 7-23. In other embodiments, N is selected to be any number from 8-10, 8-15, 8-20, or 8-23. In other embodiments, N is selected to be any number from 9-10, 9-15, 9-20, or 9-23. In other embodiments, N is selected to be any number from 10-15, 10-20, or 10-23. It will be appreciated that N can be selected to encompass similar, but higher order, ranges.

In certain embodiments, the panel of isolated biomarkers comprises one or more, two or more, three or more, four or more, or five isolated biomarkers comprising an amino acid sequence selected from AFTECCVVASQLR, ELLESYIDGR, ITLPDFTGDLR, TDAPDLPEENQAR and SFRPFVPR. In some embodiments, the panel of isolated biomarkers comprises one or more, two or more, three or more, four or more, or five isolated biomarkers comprising an amino acid sequence selected from FLNWIK, FGFGGSTDSGPIR, LLELTGPK, VEHSDLSFSK, IEGNLIFDPNNYLPK, ALVLELAK, TQILEWAAER, DVLLLVHNLPQNLPGYFWYK, SEPRPGVLLR, ITQDAQLK, ALDLSLK, WWGGQPLWITATK, and LSETNR.

In some embodiments, the panel of isolated biomarkers comprises one or more, two or more, or three of the isolated biomarkers consisting of an amino acid sequence selected from AFTECCVVASQLR, ELLESYIDGR, and ITLPDFTGDLR. In some embodiments, the panel of isolated biomarkers comprises one or more, two or more, or three of the isolated biomarkers consisting of an amino acid sequence selected from FLNWIK, FGFGGSTDSGPIR, LLELTGPK, VEHSDLSFSK, IEGNLIFDPNNYLPK, ALVLELAK, TQILEWAAER, DVLLLVHNLPQNLPGYFWYK, SEPRPGVLLR, ITQDAQLK, ALDLSLK, WWGGQPLWITATK, and LSETNR.

In some embodiments, the panel of isolated biomarkers comprises one or more, two or more, or three of the isolated biomarkers consisting of an amino acid sequence selected from the biomarkers set forth in Table 50 and the biomarkers set forth in Table 52.

In some embodiments, the panel of isolated biomarkers comprises one or more peptides comprising a fragment from lipopolysaccharide-binding protein (LBP), Schumann et al., Science 249 (4975), 1429-1431 (1990) (UniProtKB/Swiss-Prot: P18428.3); prothrombin (THRB), Walz et al., Proc. Natl. Acad. Sci. U.S.A. 74 (5), 1969-1972(1977) (NCBI Reference Sequence: NP_000497.1); complement component C5 (C5 or CO5) Haviland, J. Immunol. 146 (1), 362-368 (1991) (GenBank: AAA51925.1); plasminogen (PLMN) Petersen et al., J. Biol. Chem. 265 (11), 6104-6111(1990) (NCBI Reference Sequences: NP_000292.1 NP_001161810.1); and complement component C8 gamma chain (C8G or CO8G), Haefliger et al., Mol. Immunol. 28 (1-2), 123-131 (1991) (NCBI Reference Sequence: NP 000597.2).

In some embodiments, the panel of isolated biomarkers comprises one or more peptides comprising a fragment from cell adhesion molecule with homology to complement component 1, q subcomponent, B chain (C1QB), Reid, Biochem. J. 179 (2), 367-371 (1979) (NCBI Reference Sequence: NP_000482.3); fibrinogen beta chain (FIBB or FIB); Watt et al., Biochemistry 18 (1), 68-76 (1979) (NCBI Reference Sequences: NP_001171670.1 and NP_005132.2); C-reactive protein (CRP), Oliveira et al., J. Biol. Chem. 254 (2), 489-502 (1979) (NCBI Reference Sequence: NP_000558.2); inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4) Kim et al., Mol. Biosyst. 7 (5), 1430-1440 (2011) (NCBI Reference Sequences: NP_001159921.1 and NP_002209.2); chorionic somatomammotropin hormone (CSH) Selby et al., J. Biol. Chem. 259 (21), 13131-13138 (1984) (NCBI Reference Sequence: NP_001308.1); and angiotensinogen (ANG or ANGT) Underwood et al., Metabolism 60(8):1150-7 (2011) (NCBI Reference Sequence: NP_000020.1).

In additional embodiments, the invention provides a panel of isolated biomarkers comprising N of the biomarkers listed in Tables 1 through 63. In some embodiments, N is a number selected from the group consisting of 2 to 24. In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of AFTECCVVASQLR, ELLESYIDGR, and ITLPDFTGDLR. In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of AFTECCVVASQLR, ELLESYIDGR, ITLPDFTGDLR, TDAPDLPEENQAR and SFRPFVPR. In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of FLNWIK, FGFGGSTDSGPIR, LLELTGPK, VEHSDLSFSK, IEGNLIFDPNNYLPK, ALVLELAK, TQILEWAAER, DVLLLVHNLPQNLPGYFWYK, SEPRPGVLLR, ITQDAQLK, ALDLSLK, WWGGQPLWITATK, and LSETNR.

In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of the biomarkers set forth in Table 50 and the biomarkers set forth in Table 52.

In further embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), and complement component C8 gamma chain (C8G or CO8G). In another embodiment, the invention provides a biomarker panel comprising at least three isolated biomarkers selected from the group consisting of lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), and complement component C8 gamma chain (C8G or CO8G).

In further embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

In some embodiments, the invention provides a biomarker panel comprising lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), complement component C8 gamma chain (C8G or CO8G), complement component 1, q subcomponent, B chain (C1QB), fibrinogen beta chain (FIBB or FIB), C-reactive protein (CRP), inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), chorionic somatomammotropin hormone (CSH), and angiotensinogen (ANG or ANGT). In some embodiments, the invention provides a biomarker panel comprising Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

In another aspect, the invention provides a biomarker panel comprising at least two isolated biomarkers selected from the group consisting of lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), complement component C8 gamma chain (C8G or CO8G), complement component 1, q subcomponent, B chain (C1QB), fibrinogen beta chain (FIBB or FIB), C-reactive protein (CRP), inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), chorionic somatomammotropin hormone (CSH), and angiotensinogen (ANG or ANGT) and the biomarkers set forth in Tables 51 and 53.

In another aspect, the invention provides a biomarker panel comprising at least two isolated biomarkers selected from the group consisting of Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

It must be noted that, as used in this specification and the appended claims, the singular forms “a”, “an” and “the” include plural referents unless the content clearly dictates otherwise. Thus, for example, reference to “a biomarker” includes a mixture of two or more biomarkers, and the like.

The term “about,” particularly in reference to a given quantity, is meant to encompass deviations of plus or minus five percent.

As used in this application, including the appended claims, the singular forms “a,” “an,” and “the” include plural references, unless the content clearly dictates otherwise, and are used interchangeably with “at least one” and “one or more.”

As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “contains,” “containing,” and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, product-by-process, or composition of matter that comprises, includes, or contains an element or list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, product-by-process, or composition of matter.

As used herein, the term “panel” refers to a composition, such as an array or a collection, comprising one or more biomarkers. The term can also refer to a profile or index of expression patterns of one or more biomarkers described herein. The number of biomarkers useful for a biomarker panel is based on the sensitivity and specificity value for the particular combination of biomarker values.

As used herein, and unless otherwise specified, the terms “isolated” and “purified” generally describes a composition of matter that has been removed from its native environment (e.g., the natural environment if it is naturally occurring), and thus is altered by the hand of man from its natural state. An isolated protein or nucleic acid is distinct from the way it exists in nature.

The term “biomarker” refers to a biological molecule, or a fragment of a biological molecule, the change and/or the detection of which can be correlated with a particular physical condition or state. The terms “marker” and “biomarker” are used interchangeably throughout the disclosure. For example, the biomarkers of the present invention are correlated with an increased likelihood of preterm birth. Such biomarkers include, but are not limited to, biological molecules comprising nucleotides, nucleic acids, nucleosides, amino acids, sugars, fatty acids, steroids, metabolites, peptides, polypeptides, proteins, carbohydrates, lipids, hormones, antibodies, regions of interest that serve as surrogates for biological macromolecules and combinations thereof (e.g., glycoproteins, ribonucleoproteins, lipoproteins). The term also encompasses portions or fragments of a biological molecule, for example, peptide fragment of a protein or polypeptide that comprises at least 5 consecutive amino acid residues, at least 6 consecutive amino acid residues, at least 7 consecutive amino acid residues, at least 8 consecutive amino acid residues, at least 9 consecutive amino acid residues, at least 10 consecutive amino acid residues, at least 11 consecutive amino acid residues, at least 12 consecutive amino acid residues, at least 13 consecutive amino acid residues, at least 14 consecutive amino acid residues, at least 15 consecutive amino acid residues, at least 5 consecutive amino acid residues, at least 16 consecutive amino acid residues, at least 17 consecutive amino acid residues, at least 18 consecutive amino acid residues, at least 19 consecutive amino acid residues, at least 20 consecutive amino acid residues, at least 21 consecutive amino acid residues, at least 22 consecutive amino acid residues, at least 23 consecutive amino acid residues, at least 24 consecutive amino acid residues, at least 25 consecutive amino acid residues, or more consecutive amino acid residues.

The invention also provides a method of determining probability for preterm birth in a pregnant female, the method comprising detecting a measurable feature of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63 in a biological sample obtained from the pregnant female, and analyzing the measurable feature to determine the probability for preterm birth in the pregnant female. As disclosed herein, a measurable feature comprises fragments or derivatives of each of said N biomarkers selected from the biomarkers listed in Tables 1 through 63. In some embodiments of the disclosed methods detecting a measurable feature comprises quantifying an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63, combinations or portions and/or derivatives thereof in a biological sample obtained from said pregnant female.

The invention further provides a method of predicting GAB, the method encompassing detecting a measurable feature of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63 in a biological sample obtained from a pregnant female, and analyzing the measurable feature to predict GAB.

The invention also provides a method of predicting GAB, the method comprising: (a) quantifying in a biological sample obtained from the pregnant female an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63; (b) multiplying or thresholding the amount by a predetermined coefficient, (c) determining the predicted GAB birth in the pregnant female comprising adding the individual products to obtain a total risk score that corresponds to the predicted GAB.

The invention further provides a method of predicting time to birth in a pregnant female, the method comprising: (a) obtaining a biological sample from the pregnant female; (b) quantifying an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63 in the biological sample; (c) multiplying or thresholding the amount by a predetermined coefficient, (d) determining predicted GAB in the pregnant female comprising adding the individual products to obtain a total risk score that corresponds to the predicted GAB; and (e) subtracting the estimated gestational age (GA) at time biological sample was obtained from the predicted GAB to predict time to birth in said pregnant female. For methods directed to predicting time to birth, it is understood that “birth” means birth following spontaneous onset of labor, with or without rupture of membranes.

Although described and exemplified with reference to methods of determining probability for preterm birth in a pregnant female, the present disclosure is similarly applicable to the methods of predicting GAB, the methods for predicting term birth, methods for determining the probability of term birth in a pregnant female as well methods of predicting time to birth in a pregnant female. It will be apparent to one skilled in the art that each of the aforementioned methods has specific and substantial utilities and benefits with regard maternal-fetal health considerations.

In some embodiments, the method of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of N biomarkers, wherein N is selected from the group consisting of 2 to 24. In further embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of AFTECCVVASQLR, ELLESYIDGR, and ITLPDFTGDLR. In further embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of FLNWIK, FGFGGSTDSGPIR, LLELTGPK, VEHSDLSFSK, IEGNLIFDPNNYLPK, ALVLELAK, TQILEWAAER, DVLLLVHNLPQNLPGYFWYK, SEPRPGVLLR, ITQDAQLK, ALDLSLK, WWGGQPLWITATK, and LSETNR.

In additional embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of the biomarkers set forth in Table 50 and the biomarkers set forth in Table 52.

In additional embodiments, the method of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), and complement component C8 gamma chain (C8G or CO8G).

In additional embodiments, the method of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

In further embodiments, the disclosed method of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), complement component C8 gamma chain (C8G or CO8G), complement component 1, q subcomponent, B chain (C1QB), fibrinogen beta chain (FIBB or FIB), C-reactive protein (CRP), inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), chorionic somatomammotropin hormone (CSH), and angiotensinogen (ANG or ANGT).

In further embodiments, the disclosed method of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

In further embodiments, the disclosed method of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

In further embodiments, the disclosed method of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of the biomarkers set forth in Table 51 and the biomarkers set forth in Table 53.

In additional embodiments, the methods of determining probability for preterm birth in a pregnant female further encompass detecting a measurable feature for one or more risk indicia associated with preterm birth. In additional embodiments the risk indicia are selected form the group consisting of previous low birth weight or preterm delivery, multiple 2nd trimester spontaneous abortions, prior first trimester induced abortion, familial and intergenerational factors, history of infertility, nulliparity, placental abnormalities, cervical and uterine anomalies, gestational bleeding, intrauterine growth restriction, in utero diethylstilbestrol exposure, multiple gestations, infant sex, short stature, low prepregnancy weight, low or high body mass index, diabetes, hypertension, and urogenital infections.

A “measurable feature” is any property, characteristic or aspect that can be determined and correlated with the probability for preterm birth in a subject. The term further encompasses any property, characteristic or aspect that can be determined and correlated in connection with a prediction of GAB, a prediction of term birth, or a prediction of time to birth in a pregnant female. For a biomarker, such a measurable feature can include, for example, the presence, absence, or concentration of the biomarker, or a fragment thereof, in the biological sample, an altered structure, such as, for example, the presence or amount of a post-translational modification, such as oxidation at one or more positions on the amino acid sequence of the biomarker or, for example, the presence of an altered conformation in comparison to the conformation of the biomarker in normal control subjects, and/or the presence, amount, or altered structure of the biomarker as a part of a profile of more than one biomarker. In addition to biomarkers, measurable features can further include risk indicia including, for example, maternal characteristics, age, race, ethnicity, medical history, past pregnancy history, obstetrical history. For a risk indicium, a measurable feature can include, for example, previous low birth weight or preterm delivery, multiple 2nd trimester spontaneous abortions, prior first trimester induced abortion, familial and intergenerational factors, history of infertility, nulliparity, placental abnormalities, cervical and uterine anomalies, short cervical length measurements, gestational bleeding, intrauterine growth restriction, in utero diethylstilbestrol exposure, multiple gestations, infant sex, short stature, low prepregnancy weight/low body mass index, diabetes, hypertension, urogenital infections, hypothyroidism, asthma, low educational attainment, cigarette smoking, drug use and alcohol consumption.

In some embodiments of the disclosed methods of determining probability for preterm birth in a pregnant female, the probability for preterm birth in the pregnant female is calculated based on the quantified amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63. In some embodiments, the disclosed methods for determining the probability of preterm birth encompass detecting and/or quantifying one or more biomarkers using mass sprectrometry, a capture agent or a combination thereof.

In some embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female encompass an initial step of providing a biomarker panel comprising N of the biomarkers listed in Tables 1 through 63. In additional embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female encompass an initial step of providing a biological sample from the pregnant female.

In some embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female encompass communicating the probability to a health care provider. The disclosed of predicting GAB, the methods for predicting term birth, methods for determining the probability of term birth in a pregnant female as well methods of predicting time to birth in a pregnant female similarly encompass communicating the probability to a health care provider. As stated above, although described and exemplified with reference to determining probability for preterm birth in a pregnant female, all embodiments described throughout this disclosure are similarly applicable to the methods of predicting GAB, the methods for predicting term birth, methods for determining the probability of term birth in a pregnant female as well methods of predicting time to birth in a pregnant female. Specifically, he biomarkers and panels recited throughout this application with express reference to methods for preterm birth can also be used in methods for predicting GAB, the methods for predicting term birth, methods for determining the probability of term birth in a pregnant female as well methods of predicting time to birth in a pregnant female. It will be apparent to one skilled in the art that each of the aforementioned methods have specific and substantial utilities and benefits with regard maternal-fetal health considerations.

In additional embodiments, the communication informs a subsequent treatment decision for the pregnant female. In some embodiments, the method of determining probability for preterm birth in a pregnant female encompasses the additional feature of expressing the probability as a risk score.

As used herein, the term “risk score” refers to a score that can be assigned based on comparing the amount of one or more biomarkers in a biological sample obtained from a pregnant female to a standard or reference score that represents an average amount of the one or more biomarkers calculated from biological samples obtained from a random pool of pregnant females. Because the level of a biomarker may not be static throughout pregnancy, a standard or reference score has to have been obtained for the gestational time point that corresponds to that of the pregnant female at the time the sample was taken. The standard or reference score can be predetermined and built into a predictor model such that the comparison is indirect rather than actually performed every time the probability is determined for a subject. A risk score can be a standard (e.g., a number) or a threshold (e.g., a line on a graph). The value of the risk score correlates to the deviation, upwards or downwards, from the average amount of the one or more biomarkers calculated from biological samples obtained from a random pool of pregnant females. In certain embodiments, if a risk score is greater than a standard or reference risk score, the pregnant female can have an increased likelihood of preterm birth. In some embodiments, the magnitude of a pregnant female's risk score, or the amount by which it exceeds a reference risk score, can be indicative of or correlated to that pregnant female's level of risk.

In the context of the present invention, the term “biological sample,” encompasses any sample that is taken from pregnant female and contains one or more of the biomarkers listed in Tables 1 through 63. Suitable samples in the context of the present invention include, for example, blood, plasma, serum, amniotic fluid, vaginal secretions, saliva, and urine. In some embodiments, the biological sample is selected from the group consisting of whole blood, plasma, and serum. In a particular embodiment, the biological sample is serum. As will be appreciated by those skilled in the art, a biological sample can include any fraction or component of blood, without limitation, T cells, monocytes, neutrophils, erythrocytes, platelets and microvesicles such as exosomes and exosome-like vesicles. In a particular embodiment, the biological sample is serum.

Preterm birth refers to delivery or birth at a gestational age less than 37 completed weeks. Other commonly used subcategories of preterm birth have been established and delineate moderately preterm (birth at 33 to 36 weeks of gestation), very preterm (birth at <33 weeks of gestation), and extremely preterm (birth at ≤28 weeks of gestation). With regard to the methods disclosed herein, those skilled in the art understand that the cut-offs that delineate preterm birth and term birth as well as the cut-offs that delineate subcategories of preterm birth can be adjusted in practicing the methods disclosed herein, for example, to maximize a particular health benefit. It is further understood that such adjustments are well within the skill set of individuals considered skilled in the art and encompassed within the scope of the inventions disclosed herein. Gestational age is a proxy for the extent of fetal development and the fetus's readiness for birth. Gestational age has typically been defined as the length of time from the date of the last normal menses to the date of birth. However, obstetric measures and ultrasound estimates also can aid in estimating gestational age. Preterm births have generally been classified into two separate subgroups. One, spontaneous preterm births are those occurring subsequent to spontaneous onset of preterm labor or preterm premature rupture of membranes regardless of subsequent labor augmentation or cesarean delivery. Two, indicated preterm births are those occurring following induction or cesarean section for one or more conditions that the woman's caregiver determines to threaten the health or life of the mother and/or fetus. In some embodiments, the methods disclosed herein are directed to determining the probability for spontaneous preterm birth. In additional embodiments, the methods disclosed herein are directed to predicting gestational birth.

As used herein, the term “estimated gestational age” or “estimated GA” refers to the GA determined based on the date of the last normal menses and additional obstetric measures, ultrasound estimates or other clinical parameters including, without limitation, those described in the preceding paragraph. In contrast the term “predicted gestational age at birth” or “predicted GAB” refers to the GAB determined based on the methods of the invention as disclosed herein. As used herein, “term birth” refers to birth at a gestational age equal or more than 37 completed weeks.

In some embodiments, the pregnant female is between 17 and 28 weeks of gestation at the time the biological sample is collected. In other embodiments, the pregnant female is between 16 and 29 weeks, between 17 and 28 weeks, between 18 and 27 weeks, between 19 and 26 weeks, between 20 and 25 weeks, between 21 and 24 weeks, or between 22 and 23 weeks of gestation at the time the biological sample is collected. In further embodiments, the pregnant female is between about 17 and 22 weeks, between about 16 and 22 weeks between about 22 and 25 weeks, between about 13 and 25 weeks, between about 26 and 28, or between about 26 and 29 weeks of gestation at the time the biological sample is collected. Accordingly, the gestational age of a pregnant female at the time the biological sample is collected can be 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 or 30 weeks.

In some embodiments of the claimed methods the measurable feature comprises fragments or derivatives of each of the N biomarkers selected from the biomarkers listed in Tables 1 through 63. In additional embodiments of the claimed methods, detecting a measurable feature comprises quantifying an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63, combinations or portions and/or derivatives thereof in a biological sample obtained from said pregnant female.

The term “amount” or “level” as used herein refers to a quantity of a biomarker that is detectable or measurable in a biological sample and/or control. The quantity of a biomarker can be, for example, a quantity of polypeptide, the quantity of nucleic acid, or the quantity of a fragment or surrogate. The term can alternatively include combinations thereof. The term “amount” or “level” of a biomarker is a measurable feature of that biomarker.

In some embodiments, calculating the probability for preterm birth in a pregnant female is based on the quantified amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63. Any existing, available or conventional separation, detection and quantification methods can be used herein to measure the presence or absence (e.g., readout being present vs. absent; or detectable amount vs. undetectable amount) and/or quantity (e.g., readout being an absolute or relative quantity, such as, for example, absolute or relative concentration) of biomarkers, peptides, polypeptides, proteins and/or fragments thereof and optionally of the one or more other biomarkers or fragments thereof in samples. In some embodiments, detection and/or quantification of one or more biomarkers comprises an assay that utilizes a capture agent. In further embodiments, the capture agent is an antibody, antibody fragment, nucleic acid-based protein binding reagent, small molecule or variant thereof. In additional embodiments, the assay is an enzyme immunoassay (EIA), enzyme-linked immunosorbent assay (ELISA), and radioimmunoassay (MA). In some embodiments, detection and/or quantification of one or more biomarkers further comprises mass spectrometry (MS). In yet further embodiments, the mass spectrometry is co-immunoprecitipation-mass spectrometry (co-IP MS), where coimmunoprecipitation, a technique suitable for the isolation of whole protein complexes is followed by mass spectrometric analysis.

As used herein, the term “mass spectrometer” refers to a device able to volatilize/ionize analytes to form gas-phase ions and determine their absolute or relative molecular masses. Suitable methods of volatilization/ionization are matrix-assisted laser desorption ionization (MALDI), electrospray, laser/light, thermal, electrical, atomized/sprayed and the like, or combinations thereof. Suitable forms of mass spectrometry include, but are not limited to, ion trap instruments, quadrupole instruments, electrostatic and magnetic sector instruments, time of flight instruments, time of flight tandem mass spectrometer (TOF MS/MS), Fourier-transform mass spectrometers, Orbitraps and hybrid instruments composed of various combinations of these types of mass analyzers. These instruments can, in turn, be interfaced with a variety of other instruments that fractionate the samples (for example, liquid chromatography or solid-phase adsorption techniques based on chemical, or biological properties) and that ionize the samples for introduction into the mass spectrometer, including matrix-assisted laser desorption (MALDI), electrospray, or nanospray ionization (ESI) or combinations thereof.

Generally, any mass spectrometric (MS) technique that can provide precise information on the mass of peptides, and preferably also on fragmentation and/or (partial) amino acid sequence of selected peptides (e.g., in tandem mass spectrometry, MS/MS; or in post source decay, TOF MS), can be used in the methods disclosed herein. Suitable peptide MS and MS/MS techniques and systems are well-known per se (see, e.g., Methods in Molecular Biology, vol. 146: “Mass Spectrometry of Proteins and Peptides”, by Chapman, ed., Humana Press 2000; Biemann 1990. Methods Enzymol 193: 455-79; or Methods in Enzymology, vol. 402: “Biological Mass Spectrometry”, by Burlingame, ed., Academic Press 2005) and can be used in practicing the methods disclosed herein. Accordingly, in some embodiments, the disclosed methods comprise performing quantitative MS to measure one or more biomarkers. Such quantitiative methods can be performed in an automated (Villanueva, et al., Nature Protocols (2006) 1(2):880-891) or semi-automated format. In particular embodiments, MS can be operably linked to a liquid chromatography device (LC-MS/MS or LC-MS) or gas chromatography device (GC-MS or GC-MS/MS). Other methods useful in this context include isotope-coded affinity tag (ICAT), tandem mass tags (TMT), or stable isotope labeling by amino acids in cell culture (SILAC), followed by chromatography and MS/MS.

As used herein, the terms “multiple reaction monitoring (MRM)” or “selected reaction monitoring (SRM)” refer to an MS-based quantification method that is particularly useful for quantifying analytes that are in low abundance. In an SRM experiment, a predefined precursor ion and one or more of its fragments are selected by the two mass filters of a triple quadrupole instrument and monitored over time for precise quantification. Multiple SRM precursor and fragment ion pairs can be measured within the same experiment on the chromatographic time scale by rapidly toggling between the different precursor/fragment pairs to perform an MRM experiment. A series of transitions (precursor/fragment ion pairs) in combination with the retention time of the targeted analyte (e.g., peptide or small molecule such as chemical entity, steroid, hormone) can constitute a definitive assay. A large number of analytes can be quantified during a single LC-MS experiment. The term “scheduled,” or “dynamic” in reference to MRM or SRM, refers to a variation of the assay wherein the transitions for a particular analyte are only acquired in a time window around the expected retention time, significantly increasing the number of analytes that can be detected and quantified in a single LC-MS experiment and contributing to the selectivity of the test, as retention time is a property dependent on the physical nature of the analyte. A single analyte can also be monitored with more than one transition. Finally, included in the assay can be standards that correspond to the analytes of interest (e.g., same amino acid sequence), but differ by the inclusion of stable isotopes. Stable isotopic standards (SIS) can be incorporated into the assay at precise levels and used to quantify the corresponding unknown analyte. An additional level of specificity is contributed by the co-elution of the unknown analyte and its corresponding SIS and properties of their transitions (e.g., the similarity in the ratio of the level of two transitions of the unknown and the ratio of the two transitions of its corresponding SIS).

Mass spectrometry assays, instruments and systems suitable for biomarker peptide analysis can include, without limitation, matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) MS; MALDI-TOF post-source-decay (PSD); MALDI-TOF/TOF; surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF) MS; electrospray ionization mass spectrometry (ESI-MS); ESI-MS/MS; ESI-MS/(MS)_(n) (n is an integer greater than zero); ESI 3D or linear (2D) ion trap MS; ESI triple quadrupole MS; ESI quadrupole orthogonal TOF (Q-TOF); ESI Fourier transform MS systems; desorption/ionization on silicon (DIOS); secondary ion mass spectrometry (SIMS); atmospheric pressure chemical ionization mass spectrometry (APCI-MS); APCI-MS/MS; APCI-(MS)_(n); ion mobility spectrometry (IMS); inductively coupled plasma mass spectrometry (ICP-MS) atmospheric pressure photoionization mass spectrometry (APPI-MS); APPI-MS/MS; and APPI-(MS)_(n). Peptide ion fragmentation in tandem MS (MS/MS) arrangements can be achieved using manners established in the art, such as, e.g., collision induced dissociation (CID). As described herein, detection and quantification of biomarkers by mass spectrometry can involve multiple reaction monitoring (MRM), such as described among others by Kuhn et al. Proteomics 4: 1175-86 (2004). Scheduled multiple-reaction-monitoring (Scheduled MRM) mode acquisition during LC-MS/MS analysis enhances the sensitivity and accuracy of peptide quantitation. Anderson and Hunter, Molecular and Cellular Proteomics 5(4):573 (2006). As described herein, mass spectrometry-based assays can be advantageously combined with upstream peptide or protein separation or fractionation methods, such as for example with the chromatographic and other methods described herein below. As further described herein, shotgun quantitative proteomics can be combined with SRM/MRM-based assays for high-throughput identification and verification of prognostic biomarkers of preterm birth.

A person skilled in the art will appreciate that a number of methods can be used to determine the amount of a biomarker, including mass spectrometry approaches, such as MS/MS, LC-MS/MS, multiple reaction monitoring (MRM) or SRM and product-ion monitoring (PIM) and also including antibody based methods such as immunoassays such as Western blots, enzyme-linked immunosorbant assay (ELISA), immunoprecipitation, immunohistochemistry, immunofluorescence, radioimmunoassay, dot blotting, and FACS. Accordingly, in some embodiments, determining the level of the at least one biomarker comprises using an immunoassay and/or mass spectrometric methods. In additional embodiments, the mass spectrometric methods are selected from MS, MS/MS, LC-MS/MS, SRM, PIM, and other such methods that are known in the art. In other embodiments, LC-MS/MS further comprises 1D LC-MS/MS, 2D LC-MS/MS or 3D LC-MS/MS. Immunoassay techniques and protocols are generally known to those skilled in the art (Price and Newman, Principles and Practice of Immunoassay, 2nd Edition, Grove's Dictionaries, 1997; and Gosling, Immunoassays: A Practical Approach, Oxford University Press, 2000.) A variety of immunoassay techniques, including competitive and non-competitive immunoassays, can be used (Self et al., Curr. Opin. Biotechnol., 7:60-65 (1996).

In further embodiments, the immunoassay is selected from Western blot, ELISA, immunoprecipitation, immunohistochemistry, immunofluorescence, radioimmunoassay (MA), dot blotting, and FACS. In certain embodiments, the immunoassay is an ELISA. In yet a further embodiment, the ELISA is direct ELISA (enzyme-linked immunosorbent assay), indirect ELISA, sandwich ELISA, competitive ELISA, multiplex ELISA, ELISPOT technologies, and other similar techniques known in the art. Principles of these immunoassay methods are known in the art, for example John R. Crowther, The ELISA Guidebook, 1st ed., Humana Press 2000, ISBN 0896037282. Typically ELISAs are performed with antibodies but they can be performed with any capture agents that bind specifically to one or more biomarkers of the invention and that can be detected. Multiplex ELISA allows simultaneous detection of two or more analytes within a single compartment (e.g., microplate well) usually at a plurality of array addresses (Nielsen and Geierstanger 2004. J Immunol Methods 290: 107-20 (2004) and Ling et al. 2007. Expert Rev Mol Diagn 7: 87-98 (2007)).

In some embodiments, Radioimmunoassay (MA) can be used to detect one or more biomarkers in the methods of the invention. MA is a competition-based assay that is well known in the art and involves mixing known quantities of radioactively-labelled (e.g., ¹²⁵I or ¹³¹I-labelled) target analyte with antibody specific for the analyte, then adding non-labelled analyte from a sample and measuring the amount of labelled analyte that is displaced (see, e.g., An Introduction to Radioimmunoassay and Related Techniques, by Chard T, ed., Elsevier Science 1995, ISBN 0444821198 for guidance).

A detectable label can be used in the assays described herein for direct or indirect detection of the biomarkers in the methods of the invention. A wide variety of detectable labels can be used, with the choice of label depending on the sensitivity required, ease of conjugation with the antibody, stability requirements, and available instrumentation and disposal provisions. Those skilled in the art are familiar with selection of a suitable detectable label based on the assay detection of the biomarkers in the methods of the invention. Suitable detectable labels include, but are not limited to, fluorescent dyes (e.g., fluorescein, fluorescein isothiocyanate (FITC), Oregon Green™, rhodamine, Texas red, tetrarhodimine isothiocynate (TRITC), Cy3, Cy5, etc.), fluorescent markers (e.g., green fluorescent protein (GFP), phycoerythrin, etc.), enzymes (e.g., luciferase, horseradish peroxidase, alkaline phosphatase, etc.), nanoparticles, biotin, digoxigenin, metals, and the like.

For mass-spectrometry based analysis, differential tagging with isotopic reagents, e.g., isotope-coded affinity tags (ICAT) or the more recent variation that uses isobaric tagging reagents, iTRAQ (Applied Biosystems, Foster City, Calif.), or tandem mass tags, TMT, (Thermo Scientific, Rockford, Ill.), followed by multidimensional liquid chromatography (LC) and tandem mass spectrometry (MS/MS) analysis can provide a further methodology in practicing the methods of the invention.

A chemiluminescence assay using a chemiluminescent antibody can be used for sensitive, non-radioactive detection of protein levels. An antibody labeled with fluorochrome also can be suitable. Examples of fluorochromes include, without limitation, DAPI, fluorescein, Hoechst 33258, R-phycocyanin, B-phycoerythrin, R-phycoerythrin, rhodamine, Texas red, and lissamine. Indirect labels include various enzymes well known in the art, such as horseradish peroxidase (HRP), alkaline phosphatase (AP), beta-galactosidase, urease, and the like. Detection systems using suitable substrates for horseradish-peroxidase, alkaline phosphatase, beta-galactosidase are well known in the art.

A signal from the direct or indirect label can be analyzed, for example, using a spectrophotometer to detect color from a chromogenic substrate; a radiation counter to detect radiation such as a gamma counter for detection of ¹²⁵I; or a fluorometer to detect fluorescence in the presence of light of a certain wavelength. For detection of enzyme-linked antibodies, a quantitative analysis can be made using a spectrophotometer such as an EMAX Microplate Reader (Molecular Devices; Menlo Park, Calif.) in accordance with the manufacturer's instructions. If desired, assays used to practice the invention can be automated or performed robotically, and the signal from multiple samples can be detected simultaneously.

In some embodiments, the methods described herein encompass quantification of the biomarkers using mass spectrometry (MS). In further embodiments, the mass spectrometry can be liquid chromatography-mass spectrometry (LC-MS), multiple reaction monitoring (MRM) or selected reaction monitoring (SRM). In additional embodiments, the MRM or SRM can further encompass scheduled MRM or scheduled SRM.

As described above, chromatography can also be used in practicing the methods of the invention. Chromatography encompasses methods for separating chemical substances and generally involves a process in which a mixture of analytes is carried by a moving stream of liquid or gas (“mobile phase”) and separated into components as a result of differential distribution of the analytes as they flow around or over a stationary liquid or solid phase (“stationary phase”), between the mobile phase and said stationary phase. The stationary phase can be usually a finely divided solid, a sheet of filter material, or a thin film of a liquid on the surface of a solid, or the like. Chromatography is well understood by those skilled in the art as a technique applicable for the separation of chemical compounds of biological origin, such as, e.g., amino acids, proteins, fragments of proteins or peptides, etc.

Chromatography can be columnar (i.e., wherein the stationary phase is deposited or packed in a column), preferably liquid chromatography, and yet more preferably high-performance liquid chromatography (HPLC), or ultra high performance/pressure liquid chromatography (UHPLC). Particulars of chromatography are well known in the art (Bidlingmeyer, Practical HPLC Methodology and Applications, John Wiley & Sons Inc., 1993). Exemplary types of chromatography include, without limitation, high-performance liquid chromatography (HPLC), UHPLC, normal phase HPLC (NP-HPLC), reversed phase HPLC (RP-HPLC), ion exchange chromatography (IEC), such as cation or anion exchange chromatography, hydrophilic interaction chromatography (HILIC), hydrophobic interaction chromatography (HIC), size exclusion chromatography (SEC) including gel filtration chromatography or gel permeation chromatography, chromatofocusing, affinity chromatography such as immuno-affinity, immobilised metal affinity chromatography, and the like. Chromatography, including single-, two- or more-dimensional chromatography, can be used as a peptide fractionation method in conjunction with a further peptide analysis method, such as for example, with a downstream mass spectrometry analysis as described elsewhere in this specification.

Further peptide or polypeptide separation, identification or quantification methods can be used, optionally in conjunction with any of the above described analysis methods, for measuring biomarkers in the present disclosure. Such methods include, without limitation, chemical extraction partitioning, isoelectric focusing (IEF) including capillary isoelectric focusing (CIEF), capillary isotachophoresis (CITP), capillary electrochromatography (CEC), and the like, one-dimensional polyacrylamide gel electrophoresis (PAGE), two-dimensional polyacrylamide gel electrophoresis (2D-PAGE), capillary gel electrophoresis (CGE), capillary zone electrophoresis (CZE), micellar electrokinetic chromatography (MEKC), free flow electrophoresis (FFE), etc.

In the context of the invention, the term “capture agent” refers to a compound that can specifically bind to a target, in particular a biomarker. The term includes antibodies, antibody fragments, nucleic acid-based protein binding reagents (e.g. aptamers, Slow Off-rate Modified Aptamers (SOMAmer™)), protein-capture agents, natural ligands (i.e. a hormone for its receptor or vice versa), small molecules or variants thereof.

Capture agents can be configured to specifically bind to a target, in particular a biomarker. Capture agents can include but are not limited to organic molecules, such as polypeptides, polynucleotides and other non polymeric molecules that are identifiable to a skilled person. In the embodiments disclosed herein, capture agents include any agent that can be used to detect, purify, isolate, or enrich a target, in particular a biomarker. Any art-known affinity capture technologies can be used to selectively isolate and enrich/concentrate biomarkers that are components of complex mixtures of biological media for use in the disclosed methods.

Antibody capture agents that specifically bind to a biomarker can be prepared using any suitable methods known in the art. See, e.g., Coligan, Current Protocols in Immunology (1991); Harlow & Lane, Antibodies: A Laboratory Manual (1988); Goding, Monoclonal Antibodies: Principles and Practice (2d ed. 1986). Antibody capture agents can be any immunoglobulin or derivative thereof, whether natural or wholly or partially synthetically produced. All derivatives thereof which maintain specific binding ability are also included in the term. Antibody capture agents have a binding domain that is homologous or largely homologous to an immunoglobulin binding domain and can be derived from natural sources, or partly or wholly synthetically produced. Antibody capture agents can be monoclonal or polyclonal antibodies. In some embodiments, an antibody is a single chain antibody. Those of ordinary skill in the art will appreciate that antibodies can be provided in any of a variety of forms including, for example, humanized, partially humanized, chimeric, chimeric humanized, etc. Antibody capture agents can be antibody fragments including, but not limited to, Fab, Fab′, F(ab′)2, scFv, Fv, dsFv diabody, and Fd fragments. An antibody capture agent can be produced by any means. For example, an antibody capture agent can be enzymatically or chemically produced by fragmentation of an intact antibody and/or it can be recombinantly produced from a gene encoding the partial antibody sequence. An antibody capture agent can comprise a single chain antibody fragment. Alternatively or additionally, antibody capture agent can comprise multiple chains which are linked together, for example, by disulfide linkages.; and, any functional fragments obtained from such molecules, wherein such fragments retain specific-binding properties of the parent antibody molecule. Because of their smaller size as functional components of the whole molecule, antibody fragments can offer advantages over intact antibodies for use in certain immunochemical techniques and experimental applications.

Suitable capture agents useful for practicing the invention also include aptamers. Aptamers are oligonucleotide sequences that can bind to their targets specifically via unique three dimensional (3-D) structures. An aptamer can include any suitable number of nucleotides and different aptamers can have either the same or different numbers of nucleotides. Aptamers can be DNA or RNA or chemically modified nucleic acids and can be single stranded, double stranded, or contain double stranded regions, and can include higher ordered structures. An aptamer can also be a photoaptamer, where a photoreactive or chemically reactive functional group is included in the aptamer to allow it to be covalently linked to its corresponding target. Use of an aptamer capture agent can include the use of two or more aptamers that specifically bind the same biomarker. An aptamer can include a tag. An aptamer can be identified using any known method, including the SELEX (systematic evolution of ligands by exponential enrichment), process. Once identified, an aptamer can be prepared or synthesized in accordance with any known method, including chemical synthetic methods and enzymatic synthetic methods and used in a variety of applications for biomarker detection. Liu et al., Curr Med Chem. 18(27):4117-25 (2011). Capture agents useful in practicing the methods of the invention also include SOMAmers (Slow Off-Rate Modified Aptamers) known in the art to have improved off-rate characteristics. Brody et al., J Mol Biol. 422(5):595-606 (2012). SOMAmers can be generated using any known method, including the SELEX method.

It is understood by those skilled in the art that biomarkers can be modified prior to analysis to improve their resolution or to determine their identity. For example, the biomarkers can be subject to proteolytic digestion before analysis. Any protease can be used. Proteases, such as trypsin, that are likely to cleave the biomarkers into a discrete number of fragments are particularly useful. The fragments that result from digestion function as a fingerprint for the biomarkers, thereby enabling their detection indirectly. This is particularly useful where there are biomarkers with similar molecular masses that might be confused for the biomarker in question. Also, proteolytic fragmentation is useful for high molecular weight biomarkers because smaller biomarkers are more easily resolved by mass spectrometry. In another example, biomarkers can be modified to improve detection resolution. For instance, neuraminidase can be used to remove terminal sialic acid residues from glycoproteins to improve binding to an anionic adsorbent and to improve detection resolution. In another example, the biomarkers can be modified by the attachment of a tag of particular molecular weight that specifically binds to molecular biomarkers, further distinguishing them. Optionally, after detecting such modified biomarkers, the identity of the biomarkers can be further determined by matching the physical and chemical characteristics of the modified biomarkers in a protein database (e.g., SwissProt).

It is further appreciated in the art that biomarkers in a sample can be captured on a substrate for detection. Traditional substrates include antibody-coated 96-well plates or nitrocellulose membranes that are subsequently probed for the presence of the proteins. Alternatively, protein-binding molecules attached to microspheres, microparticles, microbeads, beads, or other particles can be used for capture and detection of biomarkers. The protein-binding molecules can be antibodies, peptides, peptoids, aptamers, small molecule ligands or other protein-binding capture agents attached to the surface of particles. Each protein-binding molecule can include unique detectable label that is coded such that it can be distinguished from other detectable labels attached to other protein-binding molecules to allow detection of biomarkers in multiplex assays. Examples include, but are not limited to, color-coded microspheres with known fluorescent light intensities (see e.g., microspheres with xMAP technology produced by Luminex (Austin, Tex.); microspheres containing quantum dot nanocrystals, for example, having different ratios and combinations of quantum dot colors (e.g., Qdot nanocrystals produced by Life Technologies (Carlsbad, Calif.); glass coated metal nanoparticles (see e.g., SERS nanotags produced by Nanoplex Technologies, Inc. (Mountain View, Calif.); barcode materials (see e.g., sub-micron sized striped metallic rods such as Nanobarcodes produced by Nanoplex Technologies, Inc.), encoded microparticles with colored bar codes (see e.g., CellCard produced by Vitra Bioscience, vitrabio.com), glass microparticles with digital holographic code images (see e.g., CyVera microbeads produced by Illumina (San Diego, Calif.); chemiluminescent dyes, combinations of dye compounds; and beads of detectably different sizes.

In another aspect, biochips can be used for capture and detection of the biomarkers of the invention. Many protein biochips are known in the art. These include, for example, protein biochips produced by Packard BioScience Company (Meriden Conn.), Zyomyx (Hayward, Calif.) and Phylos (Lexington, Mass.). In general, protein biochips comprise a substrate having a surface. A capture reagent or adsorbent is attached to the surface of the substrate. Frequently, the surface comprises a plurality of addressable locations, each of which location has the capture agent bound there. The capture agent can be a biological molecule, such as a polypeptide or a nucleic acid, which captures other biomarkers in a specific manner. Alternatively, the capture agent can be a chromatographic material, such as an anion exchange material or a hydrophilic material. Examples of protein biochips are well known in the art.

Measuring mRNA in a biological sample can be used as a surrogate for detection of the level of the corresponding protein biomarker in a biological sample. Thus, any of the biomarkers or biomarker panels described herein can also be detected by detecting the appropriate RNA. Levels of mRNA can measured by reverse transcription quantitative polymerase chain reaction (RT-PCR followed with qPCR). RT-PCR is used to create a cDNA from the mRNA. The cDNA can be used in a qPCR assay to produce fluorescence as the DNA amplification process progresses. By comparison to a standard curve, qPCR can produce an absolute measurement such as number of copies of mRNA per cell. Northern blots, microarrays, Invader assays, and RT-PCR combined with capillary electrophoresis have all been used to measure expression levels of mRNA in a sample. See Gene Expression Profiling: Methods and Protocols, Richard A. Shimkets, editor, Humana Press, 2004.

Some embodiments disclosed herein relate to diagnostic and prognostic methods of determining the probability for preterm birth in a pregnant female. The detection of the level of expression of one or more biomarkers and/or the determination of a ratio of biomarkers can be used to determine the probability for preterm birth in a pregnant female. Such detection methods can be used, for example, for early diagnosis of the condition, to determine whether a subject is predisposed to preterm birth, to monitor the progress of preterm birth or the progress of treatment protocols, to assess the severity of preterm birth, to forecast the outcome of preterm birth and/or prospects of recovery or birth at full term, or to aid in the determination of a suitable treatment for preterm birth.

The quantitation of biomarkers in a biological sample can be determined, without limitation, by the methods described above as well as any other method known in the art. The quantitative data thus obtained is then subjected to an analytic classification process. In such a process, the raw data is manipulated according to an algorithm, where the algorithm has been pre-defined by a training set of data, for example as described in the examples provided herein. An algorithm can utilize the training set of data provided herein, or can utilize the guidelines provided herein to generate an algorithm with a different set of data.

In some embodiments, analyzing a measurable feature to determine the probability for preterm birth in a pregnant female encompasses the use of a predictive model. In further embodiments, analyzing a measurable feature to determine the probability for preterm birth in a pregnant female encompasses comparing said measurable feature with a reference feature. As those skilled in the art can appreciate, such comparison can be a direct comparison to the reference feature or an indirect comparison where the reference feature has been incorporated into the predictive model. In further embodiments, analyzing a measurable feature to determine the probability for preterm birth in a pregnant female encompasses one or more of a linear discriminant analysis model, a support vector machine classification algorithm, a recursive feature elimination model, a prediction analysis of microarray model, a logistic regression model, a CART algorithm, a flex tree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, a machine learning algorithm, a penalized regression method, or a combination thereof. In particular embodiments, the analysis comprises logistic regression.

An analytic classification process can use any one of a variety of statistical analytic methods to manipulate the quantitative data and provide for classification of the sample. Examples of useful methods include linear discriminant analysis, recursive feature elimination, a prediction analysis of microarray, a logistic regression, a CART algorithm, a FlexTree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, machine learning algorithms; etc.

For creation of a random forest for prediction of GAB one skilled in the art can consider a set of k subjects (pregnant women) for whom the gestational age at birth (GAB) is known, and for whom N analytes (transitions) have been measured in a blood specimen taken several weeks prior to birth. A regression tree begins with a root node that contains all the subjects. The average GAB for all subjects can be calculated in the root node. The variance of the GAB within the root node will be high, because there is a mixture of women with different GAB's. The root node is then divided (partitioned) into two branches, so that each branch contains women with a similar GAB. The average GAB for subjects in each branch is again calculated. The variance of the GAB within each branch will be lower than in the root node, because the subset of women within each branch has relatively more similar GAB's than those in the root node. The two branches are created by selecting an analyte and a threshold value for the analyte that creates branches with similar GAB. The analyte and threshold value are chosen from among the set of all analytes and threshold values, usually with a random subset of the analytes at each node. The procedure continues recursively producing branches to create leaves (terminal nodes) in which the subjects have very similar GAB's. The predicted GAB in each terminal node is the average GAB for subjects in that terminal node. This procedure creates a single regression tree. A random forest can consist of several hundred or several thousand such trees.

Classification can be made according to predictive modeling methods that set a threshold for determining the probability that a sample belongs to a given class. The probability preferably is at least 50%, or at least 60%, or at least 70%, or at least 80% or higher. Classifications also can be made by determining whether a comparison between an obtained dataset and a reference dataset yields a statistically significant difference. If so, then the sample from which the dataset was obtained is classified as not belonging to the reference dataset class. Conversely, if such a comparison is not statistically significantly different from the reference dataset, then the sample from which the dataset was obtained is classified as belonging to the reference dataset class.

The predictive ability of a model can be evaluated according to its ability to provide a quality metric, e.g. AUROC (area under the ROC curve) or accuracy, of a particular value, or range of values. Area under the curve measures are useful for comparing the accuracy of a classifier across the complete data range. Classifiers with a greater AUC have a greater capacity to classify unknowns correctly between two groups of interest. In some embodiments, a desired quality threshold is a predictive model that will classify a sample with an accuracy of at least about 0.5, at least about 0.55, at least about 0.6, at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, at least about 0.95, or higher. As an alternative measure, a desired quality threshold can refer to a predictive model that will classify a sample with an AUC of at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, or higher.

As is known in the art, the relative sensitivity and specificity of a predictive model can be adjusted to favor either the selectivity metric or the sensitivity metric, where the two metrics have an inverse relationship. The limits in a model as described above can be adjusted to provide a selected sensitivity or specificity level, depending on the particular requirements of the test being performed. One or both of sensitivity and specificity can be at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, or higher.

The raw data can be initially analyzed by measuring the values for each biomarker, usually in triplicate or in multiple triplicates. The data can be manipulated, for example, raw data can be transformed using standard curves, and the average of triplicate measurements used to calculate the average and standard deviation for each patient. These values can be transformed before being used in the models, e.g. log-transformed, Box-Cox transformed (Box and Cox, Royal Stat. Soc., Series B, 26:211-246(1964). The data are then input into a predictive model, which will classify the sample according to the state. The resulting information can be communicated to a patient or health care provider.

To generate a predictive model for preterm birth, a robust data set, comprising known control samples and samples corresponding to the preterm birth classification of interest is used in a training set. A sample size can be selected using generally accepted criteria. As discussed above, different statistical methods can be used to obtain a highly accurate predictive model. Examples of such analysis are provided in Example 2.

In one embodiment, hierarchical clustering is performed in the derivation of a predictive model, where the Pearson correlation is employed as the clustering metric. One approach is to consider a preterm birth dataset as a “learning sample” in a problem of “supervised learning.” CART is a standard in applications to medicine (Singer, Recursive Partitioning in the Health Sciences, Springer (1999)) and can be modified by transforming any qualitative features to quantitative features; sorting them by attained significance levels, evaluated by sample reuse methods for Hotelling's T² statistic; and suitable application of the lasso method. Problems in prediction are turned into problems in regression without losing sight of prediction, indeed by making suitable use of the Gini criterion for classification in evaluating the quality of regressions.

This approach led to what is termed FlexTree (Huang, Proc. Nat. Acad. Sci. U.S.A 101:10529-10534(2004)). FlexTree performs very well in simulations and when applied to multiple forms of data and is useful for practicing the claimed methods. Software automating FlexTree has been developed. Alternatively, LARTree or LART can be used (Turnbull (2005) Classification Trees with Subset Analysis Selection by the Lasso, Stanford University). The name reflects binary trees, as in CART and FlexTree; the lasso, as has been noted; and the implementation of the lasso through what is termed LARS by Efron et al. (2004) Annals of Statistics 32:407-451 (2004). See, also, Huang et al., Proc. Natl. Acad. Sci. USA. 101(29):10529-34 (2004). Other methods of analysis that can be used include logic regression. One method of logic regression Ruczinski, Journal of Computational and Graphical Statistics 12:475-512 (2003). Logic regression resembles CART in that its classifier can be displayed as a binary tree. It is different in that each node has Boolean statements about features that are more general than the simple “and” statements produced by CART.

Another approach is that of nearest shrunken centroids (Tibshirani, Proc. Natl. Acad. Sci. U.S.A 99:6567-72(2002)). The technology is k-means-like, but has the advantage that by shrinking cluster centers, one automatically selects features, as is the case in the lasso, to focus attention on small numbers of those that are informative. The approach is available as PAM software and is widely used. Two further sets of algorithms that can be used are random forests (Breiman, Machine Learning 45:5-32 (2001)) and MART (Hastie, The Elements of Statistical Learning, Springer (2001)). These two methods are known in the art as “committee methods,” that involve predictors that “vote” on outcome.

To provide significance ordering, the false discovery rate (FDR) can be determined. First, a set of null distributions of dissimilarity values is generated. In one embodiment, the values of observed profiles are permuted to create a sequence of distributions of correlation coefficients obtained out of chance, thereby creating an appropriate set of null distributions of correlation coefficients (Tusher et al., Proc. Natl. Acad. Sci. U.S.A 98, 5116-21 (2001)). The set of null distribution is obtained by: permuting the values of each profile for all available profiles; calculating the pair-wise correlation coefficients for all profile; calculating the probability density function of the correlation coefficients for this permutation; and repeating the procedure for N times, where N is a large number, usually 300. Using the N distributions, one calculates an appropriate measure (mean, median, etc.) of the count of correlation coefficient values that their values exceed the value (of similarity) that is obtained from the distribution of experimentally observed similarity values at given significance level.

The FDR is the ratio of the number of the expected falsely significant correlations (estimated from the correlations greater than this selected Pearson correlation in the set of randomized data) to the number of correlations greater than this selected Pearson correlation in the empirical data (significant correlations). This cut-off correlation value can be applied to the correlations between experimental profiles. Using the aforementioned distribution, a level of confidence is chosen for significance. This is used to determine the lowest value of the correlation coefficient that exceeds the result that would have obtained by chance. Using this method, one obtains thresholds for positive correlation, negative correlation or both. Using this threshold(s), the user can filter the observed values of the pair wise correlation coefficients and eliminate those that do not exceed the threshold(s). Furthermore, an estimate of the false positive rate can be obtained for a given threshold. For each of the individual “random correlation” distributions, one can find how many observations fall outside the threshold range. This procedure provides a sequence of counts. The mean and the standard deviation of the sequence provide the average number of potential false positives and its standard deviation.

In an alternative analytical approach, variables chosen in the cross-sectional analysis are separately employed as predictors in a time-to-event analysis (survival analysis), where the event is the occurrence of preterm birth, and subjects with no event are considered censored at the time of giving birth. Given the specific pregnancy outcome (preterm birth event or no event), the random lengths of time each patient will be observed, and selection of proteomic and other features, a parametric approach to analyzing survival can be better than the widely applied semi-parametric Cox model. A Weibull parametric fit of survival permits the hazard rate to be monotonically increasing, decreasing, or constant, and also has a proportional hazards representation (as does the Cox model) and an accelerated failure-time representation. All the standard tools available in obtaining approximate maximum likelihood estimators of regression coefficients and corresponding functions are available with this model.

In addition the Cox models can be used, especially since reductions of numbers of covariates to manageable size with the lasso will significantly simplify the analysis, allowing the possibility of a nonparametric or semi-parametric approach to prediction of time to preterm birth. These statistical tools are known in the art and applicable to all manner of proteomic data. A set of biomarker, clinical and genetic data that can be easily determined, and that is highly informative regarding the probability for preterm birth and predicted time to a preterm birth event in said pregnant female is provided. Also, algorithms provide information regarding the probability for preterm birth in the pregnant female.

Accordingly, one skilled in the art understands that the probability for preterm birth according to the invention can be determined using either a quantitative or a categorical variable. For example, in practicing the methods of the invention the measurable feature of each of N biomarkers can be subjected to categorical data analysis to determine the probability for preterm birth as a binary categorical outcome. Alternatively, the methods of the invention may analyze the measurable feature of each of N biomarkers by initially calculating quantitative variables, in particular, predicted gestational age at birth. The predicted gestational age at birth can subsequently be used as a basis to predict risk of preterm birth. By initially using a quantitative variable and subsequently converting the quantitative variable into a categorical variable the methods of the invention take into account the continuum of measurements detected for the measurable features. For example, by predicting the gestational age at birth rather than making a binary prediction of preterm birth versus term birth, it is possible to tailor the treatment for the pregnant female. For example, an earlier predicted gestational age at birth will result in more intensive prenatal intervention, i.e. monitoring and treatment, than a predicted gestational age that approaches full term.

Among women with a predicted GAB of j days plus or minus k days, p(PTB) can estimated as the proportion of women in the PAPR clinical trial (see Example 1) with a predicted GAB of j days plus or minus k days who actually deliver before 37 weeks gestational age. More generally, for women with a predicted GAB of j days plus or minus k days, the probability that the actual gestational age at birth will be less than a specified gestational age, p(actual GAB<specified GAB), was estimated as the proportion of women in the PAPR clinical trial with a predicted GAB of j days plus or minus k days who actually deliver before the specified gestational age.

In the development of a predictive model, it can be desirable to select a subset of markers, i.e. at least 3, at least 4, at least 5, at least 6, up to the complete set of markers. Usually a subset of markers will be chosen that provides for the needs of the quantitative sample analysis, e.g. availability of reagents, convenience of quantitation, etc., while maintaining a highly accurate predictive model. The selection of a number of informative markers for building classification models requires the definition of a performance metric and a user-defined threshold for producing a model with useful predictive ability based on this metric. For example, the performance metric can be the AUC, the sensitivity and/or specificity of the prediction as well as the overall accuracy of the prediction model.

As will be understood by those skilled in the art, an analytic classification process can use any one of a variety of statistical analytic methods to manipulate the quantitative data and provide for classification of the sample. Examples of useful methods include, without limitation, linear discriminant analysis, recursive feature elimination, a prediction analysis of microarray, a logistic regression, a CART algorithm, a FlexTree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, and machine learning algorithms.

As described in Example 2, various methods are used in a training model. The selection of a subset of markers can be for a forward selection or a backward selection of a marker subset. The number of markers can be selected that will optimize the performance of a model without the use of all the markers. One way to define the optimum number of terms is to choose the number of terms that produce a model with desired predictive ability (e.g. an AUC>0.75, or equivalent measures of sensitivity/specificity) that lies no more than one standard error from the maximum value obtained for this metric using any combination and number of terms used for the given algorithm.

TABLE 1 Transitions with p-values less than 0.05 in univariate Cox Proportional Hazards analyses to predict Gestational Age at Birth p-value Cox uni- Transition Protein variate ITLPDFTGDLR_624.34_920.4 LBP_HUMAN 0.006 ELLESYIDGR_597.8_710.3 THRB_HUMAN 0.006 TDAPDLPEENQAR_728.34_613.3 CO5_HUMAN 0.007 AFTECCVVASQLR_770.87_574.3 CO5_HUMAN 0.009 SFRPFVPR_335.86_272.2 LBP_HUMAN 0.011 ITLPDFTGDLR_624.34_288.2 LBP_HUMAN 0.012 SFRPFVPR_335.86_635.3 LBP_HUMAN 0.015 ELLESYIDGR_597.8_839.4 THRB_HUMAN 0.018 LEQGENVFLQATDK_796.4_822.4 C1QB_HUMAN 0.019 ETAASLLQAGYK_626.33_679.4 THRB_HUMAN 0.021 VTGWGNLK_437.74_617.3 THRB_HUMAN 0.021 EAQLPVIENK_570.82_699.4 PLMN_HUMAN 0.023 EAQLPVIENK_570.82_329.1 PLMN_HUMAN 0.023 FLQEQGHR_338.84_497.3 CO8G_HUMAN 0.025 IRPFFPQQ_516.79_661.4 FIBB_HUMAN 0.028 ETAASLLQAGYK_626.33_879.5 THRB_HUMAN 0.029 AFTECCVVASQLR_770.87_673.4 CO5_HUMAN 0.030 TLLPVSKPEIR_418.26_288.2 CO5_HUMAN 0.030 LSSPAVITDK_515.79_743.4 PLMN_HUMAN 0.033 YEVQGEVFTKPQLWP_910.96_392.2 CRP_HUMAN 0.036 LQGTLPVEAR_542.31_571.3 CO5_HUMAN 0.036 VRPQQLVK_484.31_609.3 ITIH4_HUMAN 0.036 IEEIAAK_387.22_531.3 CO5_HUMAN 0.041 TLLPVSKPEIR_418.26_514.3 CO5_HUMAN 0.042 VQEAHLTEDQIFYFPK_655.66_701.4 CO8G_HUMAN 0.047 ISLLLIESWLEPVR_834.49_371.2 CSH_HUMAN 0.048 ALQDQLVLVAAK_634.88_289.2 ANGT_HUMAN 0.048 YEFLNGR_449.72_293.1 PLMN_HUMAN 0.049

TABLE 2 Transitions selected by the Cox stepwise AIC analysis Transition coef exp(coef) se(coef) z Pr(>|z|) Collection.Window.GA.in.Days 1.28E−01 1.14E+00 2.44E−02 5.26 1.40E−07 ITLPDFTGDLR_624.34_920.4 2.02E+00 7.52E+00 1.14E+00 1.77 0.07667 TPSAAYLWVGTGASEAEK_919.45_849.4 2.85E+01 2.44E+12 3.06E+00 9.31   <2e−16 TATSEYQTFFNPR_781.37_386.2 5.14E+00 1.70E+02 6.26E−01 8.21 2.20E−16 TASDFITK_441.73_781.4 −1.25E+00 2.86E−01 1.58E+00 −0.79 0.42856 IITGLLEFEVYLEYLQNR_738.4_530.3 1.30E+01 4.49E+05 1.45E+00 9   <2e−16 IIGGSDADIK_494.77_762.4 −6.43E+01 1.16E−28 6.64E+00 −9.68   <2e−16 YTTEIIK_434.25_603.4 6.96E+01 1.75E+30 7.06E+00 9.86   <2e−16 EDTPNSVWEPAK_686.82_315.2 7.91E+00 2.73E+03 2.66E+00 2.98 0.00293 LYYGDDEK_501.72_726.3 8.74E+00 6.23E+03 1.57E+00 5.57 2.50E−08 VRPQQLVK_484.31_609.3 4.64E+01 1.36E+20 3.97E+00 11.66   <2e−16 GGEIEGFR_432.71_379.2 −3.33E+00 3.57E−02 2.19E+00 −1.52 0.12792 DGSPDVTTADIGANTPDATK_973.45_844.4 −1.52E+01 2.51E−07 1.41E+00 −10.8   <2e−16 VQEAHLTEDQIFYFPK_655.66_391.2 −2.02E+01 1.77E−09 2.45E+00 −8.22 2.20E−16 VEIDTK_352.7_476.3 7.06E+00 1.17E+03 1.45E+00 4.86 1.20E−06 AVLTIDEK_444.76_605.3 7.85E+00 2.56E+03 9.46E−01 8.29   <2e−16 FSVVYAK_407.23_579.4 −2.44E+01 2.42E−11 3.08E+00 −7.93 2.20E−15 YYLQGAK_421.72_516.3 −1.82E+01 1.22E−08 2.45E+00 −7.44 1.00E−13 EENFYVDETTVVK_786.88_259.1 −1.90E+01 5.36E−09 2.71E+00 −7.03 2.00E−12 YGFYTHVFR_397.2_421.3 1.90E+01 1.71E+08 2.73E+00 6.93 4.20E−12 HTLNQIDEVK_598.82_951.5 1.03E+01 3.04E+04 2.11E+00 4.89 9.90E−07 AFIQLWAFDAVK_704.89_836.4 1.08E+01 4.72E+04 2.59E+00 4.16 3.20E−05 SGFSFGFK_438.72_585.3 1.35E+01 7.32E+05 2.56E+00 5.27 1.40E−07 GWVTDGFSSLK_598.8_854.4 −3.12E+00 4.42E−02 9.16E−01 −3.4 0.00066 ITENDIQIALDDAK_779.9_632.3 1.91E+00 6.78E+00 1.36E+00 1.4 0.16036

TABLE 3 Transitions selected by Cox lasso model Transition coef exp(coef) se(coef) z Pr(>|z|) Collection.Window.GA.in.Days 0.0233 1.02357 0.00928 2.51 0.012 AFTECCVVASQLR_770.87_574.3 1.07568 2.93198 0.84554 1.27 0.203 ELLESYIDGR_597.8_710.3 1.3847 3.99365 0.70784 1.96 0.05 ITLPDFTGDLR_624.34_920.4 0.814 2.25691 0.40652 2 0.045

TABLE 4 Area under the ROC (AUROC) curve for individual analytes to discriminate pre-term birth subjects from non-pre-term birth subjects. The 77 transitions with the highest AUROC area are shown. Transition AUROC ELLESYIDGR_597.8_710.3 0.71 AFTECCVVASQLR_770.87_574.3 0.70 ITLPDFTGDLR_624.34_920.4 0.70 IRPFFPQQ_516.79_661.4 0.68 TDAPDLPEENQAR_728.34_613.3 0.67 ITLPDFTGDLR_624.34_288.2 0.67 ELLESYIDGR_597.8_839.4 0.67 SFRPFVPR_335.86_635.3 0.67 ETAASLLQAGYK_626.33_879.5 0.67 TLLPVSKPEIR_418.26_288.2 0.66 ETAASLLQAGYK_626.33_679.4 0.66 SFRPFVPR_335.86_272.2 0.66 LQGTLPVEAR_542.31_571.3 0.66 VEPLYELVTATDFAYSSTVR_754.38_712.4 0.66 DPDQTDGLGLSYLSSHIANVER_796.39_328.1 0.66 VTGWGNLK_437.74_617.3 0.65 ALQDQLVLVAAK_634.88_289.2 0.65 EAQLPVIENK_570.82_329.1 0.65 VRPQQLVK_484.31_609.3 0.65 AFTECCVVASQLR_770.87_673.4 0.65 YEFLNGR_449.72_293.1 0.65 VGEYSLYIGR_578.8_871.5 0.64 EAQLPVIENK_570.82_699.4 0.64 TLLPVSKPEIR_418.26_514.3 0.64 IEEIAAK_387.22_531.3 0.64 LEQGENVFLQATDK_796.4_822.4 0.64 LQGTLPVEAR_542.31_842.5 0.64 FLQEQGHR_338.84_497.3 0.63 ISLLLIESWLEPVR_834.49_371.2 0.63 IITGLLEFEVYLEYLQNR_738.4_530.3 0.63 LSSPAVITDK_515.79_743.4 0.63 VRPQQLVK_484.31_722.4 0.63 SLPVSDSVLSGFEQR_810.92_723.3 0.63 VQEAHLTEDQIFYFPK_655.66_701.4 0.63 NADYSYSVWK_616.78_333.2 0.63 DAQYAPGYDK_564.25_813.4 0.62 FQLPGQK_409.23_276.1 0.62 TASDFITK_441.73_781.4 0.62 YGLVTYATYPK_638.33_334.2 0.62 GSFALSFPVESDVAPIAR_931.99_363.2 0.62 TLLIANETLR_572.34_703.4 0.62 VILGAHQEVNLEPHVQEIEVSR_832.78_860.4 0.62 TATSEYQTFFNPR_781.37_386.2 0.62 YEVQGEVFTKPQLWP_910.96_392.2 0.62 DISEVVTPR_508.27_472.3 0.62 GSFALSFPVESDVAPIAR_931.99_456.3 0.62 YGFYTHVFR_397.2_421.3 0.62 TLEAQLTPR_514.79_685.4 0.62 YGFYTHVFR_397.2_659.4 0.62 AVGYLITGYQR_620.84_737.4 0.61 DPDQTDGLGLSYLSSHIANVER_796.39_456.2 0.61 FNAVLTNPQGDYDTSTGK_964.46_262.1 0.61 SPEQQETVLDGNLIIR_906.48_685.4 0.61 ALNHLPLEYNSALYSR_620.99_538.3 0.61 GGEIEGFR_432.71_508.3 0.61 GIVEECCFR_585.26_900.3 0.61 DAQYAPGYDK_564.25_315.1 0.61 FAFNLYR_465.75_712.4 0.61 YTTEIIK_434.25_603.4 0.61 AVLTIDEK_444.76_605.3 0.61 AITPPHPASQANIIFDITEGNLR_825.77_459.3 0.60 EPGLCTWQSLR_673.83_790.4 0.60 AVYEAVLR_460.76_587.4 0.60 ALQDQLVLVAAK_634.88_956.6 0.60 AWVAWR_394.71_531.3 0.60 TNLESILSYPK_632.84_807.5 0.60 HLSLLTTLSNR_418.91_376.2 0.60 FTFTLHLETPKPSISSSNLNPR_829.44_787.4 0.60 AVGYLITGYQR_620.84_523.3 0.60 FQLPGQK_409.23_429.2 0.60 YGLVTYATYPK_638.33_843.4 0.60 TELRPGETLNVNFLLR_624.68_662.4 0.60 LSSPAVITDK_515.79_830.5 0.60 TATSEYQTFFNPR_781.37_272.2 0.60 LPTAVVPLR_483.31_385.3 0.60 APLTKPLK_289.86_260.2 0.60

TABLE 5 AUROCs for random forest, boosting, lasso, and logistic regression models for a specific number of transitions permitted in the model, as estimated by 100 rounds of bootstrap resampling. Number of transitions rf boosting logit lasso 1 0.59 0.67 0.64 0.69 2 0.66 0.70 0.63 0.68 3 0.69 0.70 0.58 0.71 4 0.68 0.72 0.58 0.71 5 0.73 0.71 0.58 0.68 6 0.72 0.72 0.56 0.68 7 0.74 0.70 0.60 0.67 8 0.73 0.72 0.62 0.67 9 0.72 0.72 0.60 0.67 10 0.74 0.71 0.62 0.66 11 0.73 0.69 0.58 0.67 12 0.73 0.69 0.59 0.66 13 0.74 0.71 0.57 0.66 14 0.73 0.70 0.57 0.65 15 0.72 0.70 0.55 0.64

TABLE 6 Top 15 transitions selected by each multivariate method, ranked by importance for that method. rf boosting 1 ELLESYIDGR_597.8_710.3 AFTECCVVASQLR_770.87_574.3 2 TATSEYQTFFNPR_781.37_386.2 DPDQTDGLGLSYLSSHIANVER_796.39_328.1 3 ITLPDFTGDLR_624.34_920.4 ELLESYIDGR_597.8_710.3 4 AFTECCVVASQLR_770.87_574.3 TATSEYQTFFNPR_781.37_386.2 5 VEPLYELVTATDFAYSSTVR_754.38_712.4 ITLPDFTGDLR_624.34_920.4 6 GSFALSFPVESDVAPIAR_931.99_363.2 GGEIEGFR_432.71_379.2 7 VGEYSLYIGR_578.8_871.5 ALQDQLVLVAAK_634.88_289.2 8 SFRPFVPR_335.86_635.3 VGEYSLYIGR_578.8_871.5 9 ALQDQLVLVAAK_634.88_289.2 VEPLYELVTATDFAYSSTVR_754.38_712.4 10 EDTPNSVWEPAK_686.82_315.2 SPEQQETVLDGNLIIR_906.48_685.4 11 YGFYTHVFR_397.2_421.3 YEFLNGR_449.72_293.1 12 DPDQTDGLGLSYLSSHIANVER_796.39_328.1 LEQGENVFLQATDK_796.4_822.4 13 LEQGENVFLQATDK_796.4_822.4 LQGTLPVEAR_542.31_571.3 14 LQGTLPVEAR_542.31_571.3 ISLLLIESWLEPVR_834.49_371.2 15 SFRPFVPR_335.86_272.2 TASDFITK_441.73_781.4 lasso logit 1 AFTECCVVASQLR_770.87_574.3 ALQDQLVLVAAK_634.88_289.2 2 ISLLLIESWLEPVR_834.49_371.2 AVLTIDEK_444.76_605.3 3 LPTAVVPLR_483.31_385.3 Collection.Window.GA.in.Days 4 ALQDQLVLVAAK_634.88_289.2 AHYDLR_387.7_566.3 5 ETAASLLQAGYK_626.33_679.4 AEAQAQYSAAVAK_654.33_908.5 6 IITGLLEFEVYLEYLQNR_738.4_530.3 AEAQAQYSAAVAK_654.33_709.4 7 ADSQAQLLLSTVVGVFTAPGLHLK_822.46_983.6 ADSQAQLLLSTVVGVFTAPGLHLK_822.46_983.6 8 SLPVSDSVLSGFEQR_810.92_723.3 AITPPHPASQANIIFDITEGNLR_825.77_459.3 9 SFRPFVPR_335.86_272.2 ADSQAQLLLSTVVGVFTAPGLHLK_822.46_664.4 10 IIGGSDADIK_494.77_260.2 AYSDLSR_406.2_375.2 11 NADYSYSVWK_616.78_333.2 DALSSVQESQVAQQAR_572.96_672.4 12 GSFALSFPVESDVAPIAR_931.99_456.3 ANRPFLVFIR_411.58_435.3 13 LSSPAVITDK_515.79_743.4 DALSSVQESQVAQQAR_572.96_502.3 14 ELPEHTVK_476.76_347.2 ALEQDLPVNIK_620.35_570.4 15 EAQLPVIENK_570.82_699.4 AVLTIDEK_444.76_718.4

In yet another aspect, the invention provides kits for determining probability of preterm birth, wherein the kits can be used to detect N of the isolated biomarkers listed in Tables 1 through 63. For example, the kits can be used to detect one or more, two or more, or three of the isolated biomarkers selected from the group consisting of AFTECCVVASQLR, ELLESYIDGR, and ITLPDFTGDLR. For example, the kits can be used to detect one or more, two or more, or three of the isolated biomarkers selected from the group consisting of FLNWIK, FGFGGSTDSGPIR, LLELTGPK, VEHSDLSFSK, IEGNLIFDPNNYLPK, ALVLELAK, TQILEWAAER, DVLLLVHNLPQNLPGYFWYK, SEPRPGVLLR, ITQDAQLK, ALDLSLK, WWGGQPLWITATK, and LSETNR.

In another aspect, the kits can be used to detect one or more, two or more, three or more, four or more, five or more, six or more, seven or more, or eight of the isolated biomarkers selected from the group consisting of lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), and complement component C8 gamma chain (C8G or CO8G).

In another aspect, the kits can be used to detect one or more, two or more, three or more, four or more, five or more, six or more, seven or more, or eight of the isolated biomarkers selected from the group consisting of Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

The kit can include one or more agents for detection of biomarkers, a container for holding a biological sample isolated from a pregnant female; and printed instructions for reacting agents with the biological sample or a portion of the biological sample to detect the presence or amount of the isolated biomarkers in the biological sample. The agents can be packaged in separate containers. The kit can further comprise one or more control reference samples and reagents for performing an immunoassay.

In one embodiment, the kit comprises agents for measuring the levels of at least N of the isolated biomarkers listed in Tables 1 through 63. The kit can include antibodies that specifically bind to these biomarkers, for example, the kit can contain at least one of an antibody that specifically binds to lipopolysaccharide-binding protein (LBP), an antibody that specifically binds to prothrombin (THRB), an antibody that specifically binds to complement component C5 (C5 or CO5), an antibody that specifically binds to plasminogen (PLMN), and an antibody that specifically binds to complement component C8 gamma chain (C8G or CO8G).

In one embodiment, the kit comprises agents for measuring the levels of at least N of the isolated biomarkers listed in Tables 1 through 63. The kit can include antibodies that specifically bind to these biomarkers, for example, the kit can contain at least one of an antibody that specifically binds to Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

The kit can comprise one or more containers for compositions contained in the kit. Compositions can be in liquid form or can be lyophilized. Suitable containers for the compositions include, for example, bottles, vials, syringes, and test tubes. Containers can be formed from a variety of materials, including glass or plastic. The kit can also comprise a package insert containing written instructions for methods of determining probability of preterm birth.

From the foregoing description, it will be apparent that variations and modifications can be made to the invention described herein to adopt it to various usages and conditions. Such embodiments are also within the scope of the following claims.

The recitation of a listing of elements in any definition of a variable herein includes definitions of that variable as any single element or combination (or subcombination) of listed elements. The recitation of an embodiment herein includes that embodiment as any single embodiment or in combination with any other embodiments or portions thereof.

All patents and publications mentioned in this specification are herein incorporated by reference to the same extent as if each independent patent and publication was specifically and individually indicated to be incorporated by reference.

The following examples are provided by way of illustration, not limitation.

EXAMPLES Example 1. Development of Sample Set for Discovery and Validation of Biomarkers for Preterm Birth

A standard protocol was developed governing conduct of the Proteomic Assessment of Preterm Risk (PAPR) clinical study. This protocol also specified that the samples and clinical information could be used to study other pregnancy complications for some of the subjects. Specimens were obtained from women at 11 Internal Review Board (IRB) approved sites across the United States. After providing informed consent, serum and plasma samples were obtained, as well as pertinent information regarding the patient's demographic characteristics, past medical and pregnancy history, current pregnancy history and concurrent medications. Following delivery, data were collected relating to maternal and infant conditions and complications. Serum and plasma samples were processed according to a protocol that requires standardized refrigerated centrifugation, aliquoting of the samples into 0.5 ml 2-D bar-coded cryovials and subsequent freezing at −80° C.

Following delivery, preterm birth cases were individually reviewed to determine their status as either a spontaneous preterm birth or a medically indicated preterm birth. Only spontaneous preterm birth cases were used for this analysis. For discovery of biomarkers of preterm birth, 80 samples were analyzed in two gestational age groups: a) a late window composed of samples from 23-28 weeks of gestation which included 13 cases, 13 term controls matched within one week of sample collection and 14 term random controls, and, b) an early window composed of samples from 17-22 weeks of gestation included 15 cases, 15 term controls matched within one week of sample collection and 10 random term controls.

The samples were subsequently depleted of high abundance proteins using the Human 14 Multiple Affinity Removal System (MARS 14), which removes 14 of the most abundant proteins that are treated as uninformative with regard to the identification for disease-relevant changes in the serum proteome. To this end, equal volumes of each clinical or a pooled human serum sample (HGS) sample were diluted with column buffer and filtered to remove precipitates. Filtered samples were depleted using a MARS-14 column (4.6×100 mm, Cat. #5188-6558, Agilent Technologies). Samples were chilled to 4° C. in the autosampler, the depletion column was run at room temperature, and collected fractions were kept at 4° C. until further analysis. The unbound fractions were collected for further analysis.

A second aliquot of each clinical serum sample and of each HGS was diluted into ammonium bicarbonate buffer and depleted of the 14 high and approximately 60 additional moderately abundant proteins using an IgY14-SuperMix (Sigma) hand-packed column, comprised of 10 mL of bulk material (50% slurry, Sigma). Shi et al., Methods, 56(2):246-53 (2012). Samples were chilled to 4° C. in the autosampler, the depletion column was run at room temperature, and collected fractions were kept at 4° C. until further analysis. The unbound fractions were collected for further analysis.

Depleted serum samples were denatured with trifluorethanol, reduced with dithiotreitol, alkylated using iodoacetamide, and then digested with trypsin at a 1:10 trypsin: protein ratio. Following trypsin digestion, samples were desalted on a C18 column, and the eluate lyophilized to dryness. The desalted samples were resolubilized in a reconstitution solution containing five internal standard peptides.

Depleted and trypsin digested samples were analyzed using a scheduled Multiple Reaction Monitoring method (sMRM). The peptides were separated on a 150 mm×0.32 mm Bio-Basic C18 column (ThermoFisher) at a flow rate of 5 μl/min using a Waters Nano Acquity UPLC and eluted using an acetonitrile gradient into a AB SCIEX QTRAP 5500 with a Turbo V source (AB SCIEX, Framingham, Mass.). The sMRM assay measured 1708 transitions that correspond to 854 peptides and 236 proteins. Chromatographic peaks were integrated using Rosetta Elucidator software (Ceiba Solutions).

Transitions were excluded from analysis, if their intensity area counts were less than 10000 and if they were missing in more than three samples per batch. Intensity area counts were log transformed and Mass Spectrometry run order trends and depletion batch effects were minimized using a regression analysis.

Example 2. Analysis I of Transitions to Identify Preterm Birth Biomarkers

The objective of these analyses was to examine the data collected in Example 1 to identify transitions and proteins that predict preterm birth. The specific analyses employed were (i) Cox time-to-event analyses and (ii) models with preterm birth as a binary categorical dependent variable. The dependent variable for all the Cox analyses was Gestational Age of time to event (where event is preterm birth). For the purpose of the Cox analyses, preterm birth subjects have the event on the day of birth. Term subjects are censored on the day of birth. Gestational age on the day of specimen collection is a covariate in all Cox analyses.

The assay data were previously adjusted for run order and depletion batch, and log transformed. Values for gestational age at time of sample collection were adjusted as follows. Transition values were regressed on gestational age at time of sample collection using only controls (non-pre-term subjects). The residuals from the regression were designated as adjusted values. The adjusted values were used in the models with pre-term birth as a binary categorical dependent variable. Unadjusted values were used in the Cox analyses.

Univariate Cox Proportional Hazards Analyses

Univariate Cox Proportional Hazards analyses was performed to predict Gestational Age at Birth, including Gestational age on the day of specimen collection as a covariate. Table 1 shows the transitions with p-values less than 0.05. Five proteins have multiple transitions among those with p-value less than 0.05: lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), and complement component C8 gamma chain (C8G or CO8G).

Multivariate Cox Proportional Hazards Analyses: Stepwise AIC Selection

Cox Proportional Hazards analyses was performed to predict Gestational Age at Birth, including Gestational age on the day of specimen collection as a covariate, using stepwise and lasso models for variable selection. These analyses include a total of n=80 subjects, with number of PTB events=28. The stepwise variable selection analysis used the Akaike Information Criterion (AIC) as the stopping criterion. Table 2 shows the transitions selected by the stepwise AIC analysis. The coefficient of determination (R²) for the stepwise AIC model is 0.86 (not corrected for multiple comparisons).

Multivariate Cox Proportional Hazards Analyses: Lasso Selection

Lasso variable selection was used as the second method of multivariate Cox Proportional Hazards analyses to predict Gestational Age at Birth, including Gestational age on the day of specimen collection as a covariate. This analysis uses a lambda penalty for lasso estimated by cross validation. Table 3 shows the results. The lasso variable selection method is considerably more stringent than the stepwise AIC, and selects only 3 transitions for the final model, representing 3 different proteins. These 3 proteins give the top 4 transitions from the univariate analysis; 2 of the top 4 univariate are from the same protein, and hence are not both selected by the lasso method. Lasso tends to select a relatively small number of variables with low mutual correlation. The coefficient of determination (R²) for the lasso model is 0.21 (not corrected for multiple comparisons).

Univariate AUROC Analysis of Preterm Birth as a Binary Categorical Dependent Variable

Univariate analyses was performed to discriminate pre-term subjects from non-pre-term subjects (pre-term as a binary categorical variable) as estimated by area under the receiver operating characteristic (AUROC) curve. These analyses use transition values adjusted for gestational age at time of sample collection, as described above. Table 4 shows the AUROC curve for the 77 transitions with the highest AUROC area of 0.6 or greater.

Multivariate Analysis of Preterm Birth as a Binary Categorical Dependent Variable

Multivariate analyses was performed to predict preterm birth as a binary categorical dependent variable, using random forest, boosting, lasso, and logistic regression models. Random forest and boosting models grow many classification trees. The trees vote on the assignment of each subject to one of the possible classes. The forest chooses the class with the most votes over all the trees.

For each of the four methods (random forest, boosting, lasso, and logistic regression) each method was allowed to select and rank its own best 15 transitions. We then built models with 1 to 15 transitions. Each method sequentially reduces the number of nodes from 15 to 1 independently. A recursive option was used to reduce the number of nodes at each step: To determine which node to remove, the nodes were ranked at each step based on their importance from a nested cross-validation procedure. The least important node was eliminated. The importance measures for lasso and logistic regression are z-values. For random forest and boosting, the variable importance was calculated from permuting out-of-bag data: for each tree, the classification error rate on the out-of-bag portion of the data was recorded; the error rate was then recalculated after permuting the values of each variable (i.e., transition); if the transition was in fact important, there would have been be a big difference between the two error rates; the difference between the two error rates were then averaged over all trees, and normalized by the standard deviation of the differences. The AUCs for these models are shown in Table 5, as estimated by 100 rounds of bootstrap resampling. Table 6 shows the top 15 transitions selected by each multivariate method, ranked by importance for that method. These multivariate analyses suggest that models that combine 3 or more transitions give AUC greater than 0.7, as estimated by bootstrap.

In multivariate models, random forest (rf), boosting, and lasso models gave the best area under the AUROC curve. The following transitions were selected by these models, as significant in Cox univariate models, and/or having high univariate ROC's:

AFTECCVVASQLR_770.87_574.3 ELLESYIDGR_597.8_710.3 ITLPDFTGDLR_624.34_920.4 TDAPDLPEENQAR_728.34_613.3 SFRPFVPR_335.86_635.3

In summary, univariate and multivariate Cox analyses was performed using transitions to predict Gestational Age at Birth (GAB), including Gestational age on the day of specimen collection as a covariate. In the univariate Cox analysis, five proteins were identified that have multiple transitions among those with p-value less than 0.05: lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), and complement component C8 gamma chain (C8G or CO8G).

In multivariate Cox analyses, stepwise AIC variable analysis selects 24 transitions, while the lasso model selects 3 transitions, which include the 3 top proteins in the univariate analysis. Univariate (AUROC) and multivariate (random forest, boosting, lasso, and logistic regression) analyses were performed to predict pre-term birth as a binary categorical variable. Univariate analyses identified 63 analytes with AUROC of 0.6 or greater. Multivariate analyses suggest that models that combine 3 or more transitions give AUC greater than 0.7, as estimated by bootstrap.

Example 3. Study II to Identify and Confirm Preterm Birth Biomarkers

A further study was performed using essentially the same methods described in the preceding Examples unless noted below. In this study, 2 gestational aged matched controls were used for each case of 28 cases and 56 matched controls, all from the early gestational window only (17-22 weeks).

The samples were processed in 4 batches with each batch composed of 7 cases, 14 matched controls and 3 HGS controls. Serum samples were depleted of the 14 most abundant serum samples by MARS14 as described in Example 1. Depleted serum was then reduced with dithiothreitol, alkylated with iodacetamide, and then digested with trypsin at a 1:20 trypsin to protein ratio overnight at 37° C. Following trypsin digestion, the samples were desalted on an Empore C18 96-well Solid Phase Extraction Plate (3M Company) and lyophilized to dryness. The desalted samples were resolubilized in a reconstitution solution containing five internal standard peptides.

The LC-MS/MS analysis was performed with an Agilent Poroshell 120 EC-C18 column (2.1×50 mm, 2.7 μm) and eluted with an acetonitrile gradient into a Agilent 6490 Triple Quadrapole mass spectrometer.

Data analysis included the use of conditional logistic regression where each matching triplet (case and 2 matched controls) was a stratum. The p-value reported in the table indicates whether there is a significant difference between cases and matched controls.

TABLE 7 Results of Study II Transition Protein Annotation p-value DFHINLFQVLPWLK CFAB_HUMAN Complement factor B 0.006729512 ITLPDFTGDLR LBP_HUMAN Lipopolysaccharide- 0.012907017 binding protein WWGGQPLWITATK ENPP2_HUMAN Ectonucleotide 0.013346 pyrophosphatase/phosphodiesterase family member 2 TASDFITK GELS_HUMAN Gelsolin 0.013841221 AGLLRPDYALLGHR PGRP2_HUMAN N-acetylmuramoyl-L- 0.014241979 alanine amidase FLQEQGHR CO8G_HUMAN Complement 0.014339596 component C8 gamma chain FLNWIK HABP2_HUMAN Hyaluronan-binding 0.014790418 protein 2 EKPAGGIPVLGSLVNTVLK BPIB1_HUMAN BPI fold-containing 0.019027746 family B member 1 ITGFLKPGK LBP_HUMAN Lipopolysaccharide- 0.019836986 binding protein YGLVTYATYPK CFAB_HUMAN Complement factor B 0.019927774 SLLQPNK CO8A_HUMAN Complement 0.020930939 component C8 alpha chain DISEVVTPR CFAB_HUMAN Complement factor B 0.021738046 VQEAHLTEDQIFYFPK CO8G_HUMAN Complement 0.021924548 component C8 gamma chain SPELQAEAK APOA2_HUMAN Apolipoprotein A-II 0.025944285 TYLHTYESEI ENPP2_HUMAN Ectonucleotide 0.026150038 pyrophosphatase/phosphodiesterase family member 2 DSPSVWAAVPGK PROF1_HUMAN Profilin-1 0.026607371 HYINLITR NPY_HUMAN Pro-neuropeptide Y 0.027432804 SLPVSDSVLSGFEQR CO8G_HUMAN Complement 0.029647857 component C8 gamma chain IPGIFELGISSQSDR CO8B_HUMAN Complement 0.030430996 component C8 beta chain IQTHSTTYR F13B_HUMAN Coagulation factor XIII 0.031667664 B chain DGSPDVTTADIGANTPDA PGRP2_HUMAN N-acetylmuramoyl-L- 0.034738338 TK alanine amidase QLGLPGPPDVPDHAAYHPF ITIH4_HUMAN Inter-alpha-trypsin 0.043130591 inhibitor heavy chain H4 FPLGSYTIQNIVAGSTYLF LCAP_HUMAN Leucyl-cystinyl 0.044698045 STK aminopeptidase AHYDLR FETUA_HUMAN Alpha-2-HS- 0.046259201 glycoprotein SFRPFVPR LBP_HUMAN Lipopolysaccharide- 0.047948847 binding protein

Example 4. Study III Shotgun Identification of Preterm Birth Biomarkers

A further study used a hypothesis-independent shotgun approach to identify and quantify additional biomarkers not present on our multiplexed hypothesis dependent MRM assay. Samples were processed as described in the preceding Examples unless noted below.

Tryptic digests of MARS depleted patient (preterm birth cases and term controls) samples were fractionated by two-dimensional liquid chromatography and analyzed by tandem mass spectrometry. Aliquots of the samples, equivalent to 3-4 μl of serum, were injected onto a 6 cm×75 μm self-packed strong cation exchange (Luna SCX, Phenomenex) column. Peptides were eluded from the SCX column with salt (15, 30, 50, 70, and 100% B, where B=250 mM ammonium acetate, 2% acetonitrile, 0.1% formic acid in water) and consecutively for each salt elution, were bound to a 0.5 μl C18 packed stem trap (Optimize Technologies, Inc.) and further fractionated on a 10 cm×75 μm reversed phase ProteoPep II PicoFrit column (New Objective). Peptides were eluted from the reversed phase column with an acetonitrile gradient containing 0.1% formic acid and directly ionized on an LTQ-Orbitrap (ThermoFisher). For each scan, peptide parent ion masses were obtained in the Orbitrap at 60K resolution and the top seven most abundant ions were fragmented in the LTQ to obtain peptide sequence information.

Parent and fragment ion data were used to search the Human RefSeq database using the Sequest (Eng et al., J. Am. Soc. Mass Spectrom 1994; 5:976-989) and X! Tandem (Craig and Beavis, Bioinformatics 2004; 20:1466-1467) algorithms. For Sequest, data was searched with a 20 ppm tolerance for the parent ion and 1 AMU for the fragment ion. Two missed trypsin cleavages were allowed, and modifications included static cysteine carboxyamidomethylation and methionine oxidation. After searching the data was filtered by charge state vs. Xcorr scores (charge+1≥1.5 Xcorr, charge+2≥2.0, charge+3≥2.5). Similar search parameters were used for X!tandem, except the mass tolerance for the fragment ion was 0.8 AMU and there is no Xcorr filtering. Instead, the PeptideProphet algorithm (Keller et al., Anal. Chem 2002; 74:5383-5392) was used to validate each X!Tandem peptide-spectrum assignment and Protein assignments were validated using ProteinProphet algorithm (Nesvizhskii et al., Anal. Chem 2002; 74:5383-5392). Data was filtered to include only the peptide-spectrum matches that had PeptideProphet probability of 0.9 or more. After compiling peptide and protein identifications, spectral count data for each peptide were imported into DAnTE software (Polpitiya et al., Bioinformatics. 2008; 24:1556-1558). Log transformed data was mean centered and missing values were filtered, by requiring that a peptide had to be identified in at least 4 cases and 4 controls. To determine the significance of an analyte, Receiver Operating Characteristic (ROC) curves for each analyte were created where the true positive rate (Sensitivity) is plotted as a function of the false positive rate (1-Specificity) for different thresholds that separate the SPTB and Term groups. The area under the ROC curve (AUC) is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one. Peptides with AUC greater than or equal to 0.6 found uniquely by Sequest or Xtandem are found in Tables 8 and 9, respectively, and those identified by both approaches are found in Table 10.

TABLE 8 Significant peptides (AUC > 0.6) for Sequest only Protein Description Uniprot ID (name) Peptide S_AUC 5′-AMP-activated Q9UGI9 (AAKG3_HUMAN) K.LVIFDTM*LEIK.K 0.78 protein kinase subunit gamma-3 afamin precursor P43652 (AFAM_HUMAN) K.FIEDNIEYITIIAFAQYVQEATFEEME 0.79 K.L afamin precursor P43652 (AFAM_HUMAN) K.IAPQLSTEELVSLGEK.M 0.71 afamin precursor P43652 (AFAM_HUMAN) K.LKHELTDEELQSLFTNFANVVDK.C 0.60 afamin precursor P43652 (AFAM_HUMAN) K.LPNNVLQEK.I 0.60 afamin precursor P43652 (AFAM_HUMAN) K.SDVGFLPPFPTLDPEEK.C 0.71 afamin precursor P43652 (AFAM_HUMAN) K.VMNHICSK.Q 0.68 afamin precursor P43652 (AFAM_HUMAN) R.ESLLNHFLYEVAR.R 0.69 afamin precursor P43652 (AFAM_HUMAN) R.LCFFYNKK.S 0.69 alpha-1- P01011 (AACT_HUMAN) K.AVLDVFEEGTEASAATAVK.I 0.72 antichymotrypsin precursor alpha-1- P01011 (AACT_HUMAN) K.EQLSLLDR.F 0.65 antichymotrypsin precursor alpha-1- P01011 (AACT_HUMAN) K.EQLSLLDRFTEDAK.R 0.64 antichymotrypsin precursor alpha-1- P01011 (AACT_HUMAN) K.EQLSLLDRFTEDAKR.L 0.60 antichymotrypsin precursor alpha-1- P01011 (AACT_HUMAN) K.ITDLIKDLDSQTMM*VLVNYIFFK.A 0.65 antichymotrypsin precursor alpha-1- P01011 (AACT_HUMAN) K.ITLLSALVETR.T 0.62 antichymotrypsin precursor alpha-1- P01011 (AACT_HUMAN) K.RLYGSEAFATDFQDSAAAK.K 0.62 antichymotrypsin precursor alpha-1- P01011 (AACT_HUMAN) R.EIGELYLPK.F 0.65 antichymotrypsin precursor alpha-1B- P04217 (A1BG_HUMAN) R.CEGPIPDVTFELLR.E 0.67 glycoprotein precursor alpha-1B- P04217 (A1BG_HUMAN) R.FALVR.E 0.79 glycoprotein precursor alpha-2-antiplasmin P08697 (A2AP_HUMAN) K.SPPGVCSR.D 0.81 isoform a precursor alpha-2-antiplasmin P08697 (A2AP_HUMAN) R.DSFHLDEQFTVPVEMMQAR.T 0.69 isoform a precursor alpha-2-HS- P02765 (FETUA_HUMAN) K.CNLLAEK.Q 0.67 glycoprotein preproprotein alpha-2-HS- P02765 (FETUA_HUMAN) K.EHAVEGDCDFQLLK.L 0.67 glycoprotein preproprotein alpha-2-HS- P02765 (FETUA_HUMAN) K.HTLNQIDEVKVWPQQPSGELFEIEID 0.64 glycoprotein TLETTCHVLDPTPVAR.C preproprotein alpha-2- P01023 (A2MG_HUMAN) K.MVSGFIPLKPTVK.M 0.73 macroglobulin precursor alpha-2- P01023 (A2MG_HUMAN) R.AFQPFFVELTM*PYSVIR.G 0.68 macroglobulin precursor alpha-2- P01023 (A2MG_HUMAN) R.AFQPFFVELTMPYSVIR.G 0.62 macroglobulin precursor alpha-2- P01023 (A2MG_HUMAN) R.NQGNTWLTAFVLK.T 0.73 macroglobulin precursor angiotensinogen P01019 (ANGT_HUMAN) K.IDRFMQAVTGWK.T 0.81 preproprotein angiotensinogen P01019 (ANGT_HUMAN) K.LDTEDKLR.A 0.72 preproprotein angiotensinogen P01019 (ANGT_HUMAN) K.TGCSLMGASVDSTLAFNTYVHFQGK 0.64 preproprotein .M angiotensinogen P01019 (ANGT_HUMAN) R.AAMVGMLANFLGFR.I 0.62 preproprotein antithrombin-III P01008 (ANT3_HUMAN) K.NDNDNIFLSPLSISTAFAMTK.L 0.64 precursor antithrombin-III P01008 (ANT3_HUMAN) K.SKLPGIVAEGRDDLYVSDAFHK.A 0.81 precursor antithrombin-III P01008 (ANT3_HUMAN) R.EVPLNTIIFMGR.V 0.61 precursor antithrombin-III P01008 (ANT3_HUMAN) R.FATTFYQHLADSKNDNDNIFLSPLSIS 0.66 precursor TAFAMTK.L antithrombin-III P01008 (ANT3_HUMAN) R.ITDVIPSEAINELTVLVLVNTIYFK.G 0.60 precursor antithrombin-III P01008 (ANT3_HUMAN) R.RVWELSK.A 0.63 precursor antithrombin-III P01008 (ANT3_HUMAN) R.VAEGTQVLELPFKGDDITM*VLILPK 0.62 precursor PEK.S antithrombin-III P01008 (ANT3_HUMAN) R.VAEGTQVLELPFKGDDITMVLILPKP 0.62 precursor EK.S apolipoprotein A-II P02652 (APOA2_HUMAN) K.AGTELVNFLSYFVELGTQPATQ.- 0.61 preproprotein apolipoprotein A-II P02652 (APOA2_HUMAN) K.EPCVESLVSQYFQTVTDYGK.D 0.63 preproprotein apolipoprotein A-IV P06727 (APOA4_HUMAN) K.ALVQQMEQLR.Q 0.61 precursor apolipoprotein A-IV P06727 (APOA4_HUMAN) K.LGPHAGDVEGHLSFLEK.D 0.61 precursor apolipoprotein A-IV P06727 (APOA4_HUMAN) K.SELTQQLNALFQDK.L 0.71 precursor apolipoprotein A-IV P06727 (APOA4_HUMAN) K.SLAELGGHLDQQVEEFRR.R 0.61 precursor apolipoprotein A-IV P06727 (APOA4_HUMAN) K.VKIDQTVEELRR.S 0.75 precursor apolipoprotein A-IV P06727 (APOA4_HUMAN) K.VNSFFSTFK.E 0.63 precursor apolipoprotein B-100 P04114 (APOB_HUMAN) K.ATFQTPDFIVPLTDLR.I 0.65 precursor apolipoprotein B-100 P04114 (APOB_HUMAN) K.AVSM*PSFSILGSDVR.V 0.65 precursor apolipoprotein B-100 P04114 (APOB_HUMAN) K.AVSMPSFSILGSDVR.V 0.67 precursor apolipoprotein B-100 P04114 (APOB_HUMAN) K.EQHLFLPFSYK.N 0.65 precursor apolipoprotein B-100 P04114 (APOB_HUMAN) K.KIISDYHQQFR.Y 0.63 precursor apolipoprotein B-100 P04114 (APOB_HUMAN) K.QVFLYPEKDEPTYILNIK.R 0.64 precursor apolipoprotein B-100 P04114 (APOB_HUMAN) K.SPAFTDLHLR.Y 0.69 precursor apolipoprotein B-100 P04114 (APOB_HUMAN) K.TILGTMPAFEVSLQALQK.A 0.62 precursor apolipoprotein B-100 P04114 (APOB_HUMAN) K.VLADKFIIPGLK.L 0.72 precursor apolipoprotein B-100 P04114 (APOB_HUMAN) K.YSQPEDSLIPFFEITVPESQLTVSQFTL 0.61 precursor PK.S apolipoprotein B-100 P04114 (APOB_HUMAN) R.DLKVEDIPLAR.I 0.64 precursor apolipoprotein B-100 P04114 (APOB_HUMAN) R.GIISALLVPPETEEAK.Q 0.81 precursor apolipoprotein B-100 P04114 (APOB_HUMAN) R.ILGEELGFASLHDLQLLGK.L 0.62 precursor apolipoprotein B-100 P04114 (APOB_HUMAN) R.LELELRPTGEIEQYSVSATYELQR.E 0.60 precursor apolipoprotein B-100 P04114 (APOB_HUMAN) R.NIQEYLSILTDPDGK.G 0.68 precursor apolipoprotein B-100 P04114 (APOB_HUMAN) R.TFQIPGYTVPVVNVEVSPFTIEMSAF 0.75 precursor GYVFPK.A apolipoprotein B-100 P04114 (APOB_HUMAN) R.TIDQMLNSELQWPVPDIYLR.D 0.70 precursor apolipoprotein C-I P02654 (APOC1_HUMAN) K.MREWFSETFQK.V 0.61 precursor apolipoprotein C-II P02655 (APOC2_HUMAN) K.STAAMSTYTGIFTDQVLSVLKGEE.- 0.61 precursor apolipoprotein C-III P02656 (APOC3_HUMAN) R.GWVTDGFSSLK.D 0.62 precursor apolipoprotein E P02649 (APOE_HUMAN) R.AATVGSLAGQPLQER.A 0.61 precursor apolipoprotein E P02649 (APOE_HUMAN) R.LKSWFEPLVEDMQR.Q 0.65 precursor apolipoprotein E P02649 (APOE_HUMAN) R.WVQTLSEQVQEELLSSQVTQELR.A 0.64 precursor ATP-binding cassette O14678 (ABCD4_HUMAN) K.LCGGGRWELM*R.I 0.60 sub-family D member 4 ATP-binding cassette Q9NUQ8 (ABCF3_HUMAN) K.LPGLLK.R 0.73 sub-family F member 3 beta-2-glycoprotein 1 P02749 (APOH_HUMAN) K.EHSSLAFWK.T 0.64 precursor beta-2-glycoprotein 1 P02749 (APOH_HUMAN) R.TCPKPDDLPFSTVVPLK.T 0.60 precursor beta-2-glycoprotein 1 P02749 (APOH_HUMAN) R.VCPFAGILENGAVR.Y 0.68 precursor beta-Ala-His Q96KN2 (CNDP1_HUMAN) K.LFAAFFLEMAQLH.- 0.68 dipeptidase precursor biotinidase precursor P43251 (BTD_HUMAN) K.SHLIIAQVAK.N 0.62 carboxypeptidase B2 Q96IY4 (CBPB2_HUMAN) K.NAIWIDCGIHAR.E 0.62 preproprotein carboxypeptidase N P15169 (CBPN_HUMAN) R.EALIQFLEQVHQGIK.G 0.69 catalytic chain precursor carboxypeptidase N P22792 (CPN2_HUMAN) R.LLNIQTYCAGPAYLK.G 0.62 subunit 2 precursor catalase P04040 (CATA_HUMAN) R.LCENIAGHLKDAQIFIQK.K 0.62 ceruloplasmin P00450 (CERU_HUMAN) K.AETGDKVYVHLK.N 0.61 precursor ceruloplasmin P00450 (CERU_HUMAN) K.AGLQAFFQVQECNK.S 0.62 precursor ceruloplasmin P00450 (CERU_HUMAN) K.DIASGLIGPLIICK.K 0.63 precursor ceruloplasmin P00450 (CERU_HUMAN) K.DIFTGLIGPM*K.I 0.63 precursor ceruloplasmin P00450 (CERU_HUMAN) K.DIFTGLIGPMK.I 0.68 precursor ceruloplasmin P00450 (CERU_HUMAN) K.M*YYSAVDPTKDIFTGLIGPMK.I 0.62 precursor ceruloplasmin P00450 (CERU_HUMAN) K.MYYSAVDPTKDIFTGLIGPM*K.I 0.63 precursor ceruloplasmin P00450 (CERU_HUMAN) K.PVWLGFLGPIIK.A 0.63 precursor ceruloplasmin P00450 (CERU_HUMAN) R.ADDKVYPGEQYTYMLLATEEQSPGE 0.64 precursor GDGNCVTR.I ceruloplasmin P00450 (CERU_HUMAN) R.DTANLFPQTSLTLHM*WPDTEGTF 0.71 precursor NVECLTTDHYTGGMK.Q ceruloplasmin P00450 (CERU_HUMAN) R.DTANLFPQTSLTLHMWPDTEGTFN 0.68 precursor VECLTTDHYTGGMK.Q ceruloplasmin P00450 (CERU_HUMAN) R.FNKNNEGTYYSPNYNPQSR.S 0.74 precursor ceruloplasmin P00450 (CERU_HUMAN) R.IDTINLFPATLFDAYM*VAQNPGEW 0.75 precursor M*LSCQNLNHLK.A ceruloplasmin P00450 (CERU_HUMAN) R.IDTINLFPATLFDAYM*VAQNPGEW 0.86 precursor MLSCQNLNHLK.A ceruloplasmin P00450 (CERU_HUMAN) R.IDTINLFPATLFDAYMVAQNPGEW 0.60 precursor M*LSCQNLNHLK.A ceruloplasmin P00450 (CERU_HUMAN) R.KAEEEHLGILGPQLHADVGDKVK.I 0.71 precursor ceruloplasmin P00450 (CERU_HUMAN) R.TTIEKPVWLGFLGPIIK.A 0.63 precursor cholinesterase P06276 (CHLE_HUMAN) R.FWTSFFPK.V 0.76 precursor clusterin P10909 (CLUS_HUMAN) K.LFDSDPITVTVPVEVSR.K 0.78 preproprotein clusterin P10909 (CLUS_HUMAN) R.ASSIIDELFQDR.F 0.68 preproprotein coagulation factor IX P00740 (FA9_HUMAN) K.WIVTAAHCVETGVK.I 0.60 preproprotein coagulation factor VII P08709 (FA7_HUMAN) R.FSLVSGWGQLLDR.G 0.78 isoform a preproprotein coagulation factor X P00742 (FA10_HUMAN) K.ETYDFDIAVLR.L 0.75 preproprotein coiled-coil domain- Q8IYE1 (CCD13_HUMAN) K.VRQLEMEIGQLNVHYLR.N 0.67 containing protein 13 complement C1q P02745 (C1QA_HUMAN) R.PAFSAIR.R 0.66 subcomponent subunit A precursor complement C1q P02746 (C1QB_HUMAN) K.VVTFCDYAYNTFQVTTGGMVLK.L 0.63 subcomponent subunit B precursor complement C1q P02747 (C1QC_HUMAN) K.FQSVFTVTR.Q 0.63 subcomponent subunit C precursor complement C1r P00736 (C1R_HUMAN) K.TLDEFTIIQNLQPQYQFR.D 0.62 subcomponent precursor complement C1r P00736 (C1R_HUMAN) R.MDVFSQNMFCAGHPSLK.Q 0.68 subcomponent precursor complement C1r P00736 (C1R_HUMAN) R.WILTAAHTLYPK.E 0.74 subcomponent precursor complement C1s P09871 (C1S_HUMAN) K.FYAAGLVSWGPQCGTYGLYTR.V 0.68 subcomponent precursor complement C1s P09871 (C1S_HUMAN) K.GFQVVVTLR.R 0.63 subcomponent precursor complement C2 P06681 (CO2_HUMAN) R.GALISDQWVLTAAHCFR.D 0.61 isoform 3 complement C2 P06681 (CO2_HUMAN) R.PICLPCTMEANLALR.R 0.66 isoform 3 complement C3 P01024 (CO3_HUMAN) R.YYGGGYGSTQATFMVFQALAQYQK 0.75 precursor .D complement C4-A P0C0L4 (CO4A_HUMAN) K.GLCVATPVQLR.V 0.74 isoform 1 complement C4-A P0C0L4 (CO4A_HUMAN) K.M*RPSTDTITVM*VENSHGLR.V 0.83 isoform 1 complement C4-A P0C0L4 (CO4A_HUMAN) K.MRPSTDTITVM*VENSHGLR.V 0.72 isoform 1 complement C4-A P0C0L4 (CO4A_HUMAN) K.VGLSGM*AIADVTLLSGFHALR.A 0.71 isoform 1 complement C4-A P0C0L4 (CO4A_HUMAN) K.VLSLAQEQVGGSPEK.L 0.63 isoform 1 complement C4-A P0C0L4 (CO4A_HUMAN) R.EMSGSPASGIPVK.V 0.65 isoform 1 complement C4-A P0C0L4 (CO4A_HUMAN) R.GCGEQTM*IYLAPTLAASR.Y 0.75 isoform 1 complement C4-A P0C0L4 (CO4A_HUMAN) R.GLQDEDGYR.M 0.75 isoform 1 complement C4-A P0C0L4 (CO4A_HUMAN) R.GQIVFMNREPK.R 0.93 isoform 1 complement C4-A P0C0L4 (CO4A_HUMAN) R.KKEVYM*PSSIFQDDFVIPDISEPGT 0.72 isoform 1 WK.I complement C4-A P0C0L4 (CO4A_HUMAN) R.LPMSVR.R 0.78 isoform 1 complement C4-A P0C0L4 (CO4A_HUMAN) R.LTVAAPPSGGPGFLSIER.P 0.84 isoform 1 complement C4-A P0C0L4 (CO4A_HUMAN) R.NFLVR.A 0.75 isoform 1 complement C4-A P0C0L4 (CO4A_HUMAN) R.NGESVKLHLETDSLALVALGALDTAL 0.88 isoform 1 YAAGSK.S complement C4-A P0C0L4 (CO4A_HUMAN) R.QGSFQGGFR.S 0.60 isoform 1 complement C4-A P0C0L4 (CO4A_HUMAN) R.TLEIPGNSDPNMIPDGDFNSYVR.V 0.69 isoform 1 complement C4-A P0C0L4 (CO4A_HUMAN) R.VTASDPLDTLGSEGALSPGGVASLLR 0.63 isoform 1 .L complement C4-A P0C0L4 (CO4A_HUMAN) R.YLDKTEQWSTLPPETK.D 0.67 isoform 1 complement C5 P01031 (CO5_HUMAN) K.ADNFLLENTLPAQSTFTLAISAYALSL 0.63 preproprotein GDK.T complement C5 P01031 (CO5_HUMAN) K.ALVEGVDQLFTDYQIK.D 0.63 preproprotein complement C5 P01031 (CO5_HUMAN) K.DGHVILQLNSIPSSDFLCVR.F 0.62 preproprotein complement C5 P01031 (CO5_HUMAN) K.DVFLEMNIPYSVVR.G 0.63 preproprotein complement C5 P01031 (CO5_HUMAN) K.EFPYRIPLDLVPK.T 0.60 preproprotein complement C5 P01031 (CO5_HUMAN) K.FQNSAILTIQPK.Q 0.67 preproprotein complement C5 P01031 (CO5_HUMAN) K.VFKDVFLEMNIPYSVVR.G 0.63 preproprotein complement C5 P01031 (CO5_HUMAN) R.VFQFLEK.S 0.61 preproprotein complement P13671 (CO6_HUMAN) K.DLHLSDVFLK.A 0.60 component C6 precursor complement P13671 (CO6_HUMAN) R.TECIKPVVQEVLTITPFQR.L 0.62 component C6 precursor complement P10643 (CO7_HUMAN) K.SSGWHFVVK.F 0.61 component C7 precursor complement P10643 (CO7_HUMAN) R.ILPLTVCK.M 0.75 component C7 precursor complement P07357 (CO8A_HUMAN) R.ALDQYLMEFNACR.C 0.65 component C8 alpha chain precursor complement P07360 (CO8G_HUMAN) K.YGFCEAADQFHVLDEVR.R 0.60 component C8 gamma chain precursor complement P02748 (CO9_HUMAN) R.AIEDYINEFSVRK.0 0.69 component C9 precursor complement P02748 (CO9_HUMAN) R.TAGYGINILGMDPLSTPFDNEFYNGL 0.69 component C9 CNR.D precursor complement factor B P00751 (CFAB_HUMAN) K.ALFVSEEEKK.L 0.64 preproprotein complement factor B P00751 (CFAB_HUMAN) K.CLVNLIEK.V 0.70 preproprotein complement factor B P00751 (CFAB_HUMAN) K.EAGIPEFYDYDVALIK.L 0.66 preproprotein complement factor B P00751 (CFAB_HUMAN) K.VSEADSSNADWVTK.Q 0.73 preproprotein complement factor B P00751 (CFAB_HUMAN) K.YGQTIRPICLPCTEGTTR.A 0.67 preproprotein complement factor B P00751 (CFAB_HUMAN) R.DLEIEVVLFHPNYNINGK.K 0.71 preproprotein complement factor B P00751 (CFAB_HUMAN) R.FLCTGGVSPYADPNTCR.G 0.64 preproprotein complement factor H P08603 (CFAH_HUMAN) K.DGWSAQPTCIK.S 0.80 isoform a precursor complement factor H P08603 (CFAH_HUMAN) K.EGWIHTVCINGR.W 0.67 isoform a precursor complement factor H P08603 (CFAH_HUMAN) K.TDCLSLPSFENAIPMGEK.K 0.61 isoform a precursor complement factor H P08603 (CFAH_HUMAN) R.DTSCVNPPTVQNAYIVSR.Q 0.60 isoform a precursor complement factor H P08603 (CFAH_HUMAN) K.CTSTGWIPAPR.0 0.68 isoform b precursor complement factor H P08603 (CFAH_HUMAN) K.IIYKENER.F 0.76 isoform b precursor complement factor H P08603 (CFAH_HUMAN) K.IVSSAM*EPDREYHFGQAVR.F 0.75 isoform b precursor complement factor H P08603 (CFAH_HUMAN) K.IVSSAMEPDREYHFGQAVR.F 0.68 isoform b precursor complement factor H P08603 (CFAH_HUMAN) R.CTLKPCDYPDIK.H 0.81 isoform b precursor complement factor H P08603 (CFAH_HUMAN) R.KGEWVALNPLR.K 0.60 isoform b precursor complement factor H P08603 (CFAH_HUMAN) R.KGEWVALNPLRK.0 0.69 isoform b precursor complement factor H P08603 (CFAH_HUMAN) R.RPYFPVAVGK.Y 0.68 isoform b precursor complement factor Q03591 (FHR1_HUMAN) R.EIMENYNIALR.W 0.64 H-related protein 1 precursor complement factor I P05156 (CFAI_HUMAN) K.DASGITCGGIYIGGCWILTAAHCLR.A 0.71 preproprotein complement factor I P05156 (CFAI_HUMAN) K.VANYFDWISYHVGR.P 0.72 preproprotein complement factor I P05156 (CFAI_HUMAN) R.IIFHENYNAGTYQNDIALIEMK.K 0.63 preproprotein complement factor I P05156 (CFAI_HUMAN) R.YQIWTTVVDWIHPDLK.R 0.63 preproprotein conserved oligomeric Q9Y2V7 (COG6_HUMAN) K.ISNLLK.F 0.65 Golgi complex subunit 6 isoform corticosteroid- P08185 (CBG_HUMAN) R.WSAGLTSSQVDLYIPK.V 0.62 binding globulin precursor C-reactive protein P02741 (CRP_HUMAN) K.YEVQGEVFTKPQLWP.- 0.60 precursor dopamine beta- P09172 (DOPO_HUMAN) R.HVLAAWALGAK.A 0.88 hydroxylase precursor double-stranded Q9NS39 (RED2_HUMAN) R.AGLRYVCLAEPAER.R 0.75 RNA-specific editase B2 dual oxidase 2 Q9NRD8 (DUOX2_HUMAN) R.FTQLCVKGGGGGGNGIR.D 0.65 precursor FERM domain- Q9BZ67 (FRMD8_HUMAN) R.VQLGPYQPGRPAACDLR.E 0.65 containing protein 8 fetuin-B precursor Q9UGM5 (FETUB_HUMAN) R.GGLGSLFYLTLDVLETDCHVLR.K 0.83 ficolin-3 isoform 1 O75636 (FCN3_HUMAN) R.ELLSQGATLSGWYHLCLPEGR.A 0.69 precursor gastric intrinsic factor P27352 (IF_HUMAN) K.KTTDM*ILNEIKQGK.F 0.60 precursor gelsolin isoform d P06396 (GELS_HUMAN) K.NWRDPDQTDGLGLSYLSSHIANVER 0.72 .V gelsolin isoform d P06396 (GELS_HUMAN) K.TPSAAYLWVGTGASEAEK.T 0.80 gelsolin isoform d P06396 (GELS_HUMAN) R.VEKFDLVPVPTNLYGDFFTGDAYVIL 0.60 K.T gelsolin isoform d P06396 (GELS_HUMAN) R.VPFDAATLHTSTAMAAQHGMDDD 0.67 GTGQK.Q glutathione P22352 (GPX3_HUMAN) K.FYTFLK.N 0.63 peroxidase 3 precursor hemopexin precursor P02790 (HEMO_HUMAN) K.GDKVWVYPPEKK.E 0.65 hemopexin precursor P02790 (HEMO_HUMAN) K.LLQDEFPGIPSPLDAAVECHR.G 0.71 hemopexin precursor P02790 (HEMO_HUMAN) K.SGAQATWTELPWPHEK.V 0.64 hemopexin precursor P02790 (HEMO_HUMAN) K.SGAQATWTELPWPHEKVDGALCM 0.61 EK.S hemopexin precursor P02790 (HEMO_HUMAN) K.VDGALCMEK.S 0.66 hemopexin precursor P02790 (HEMO_HUMAN) R.DYFMPCPGR.G 0.68 hemopexin precursor P02790 (HEMO_HUMAN) R.EWFWDLATGTM*K.E 0.64 hemopexin precursor P02790 (HEMO_HUMAN) R.QGHNSVFLIK.G 0.71 heparin cofactor 2 P05546 (HEP2_HUMAN) K.HQGTITVNEEGTQATTVTTVGFMPL 0.60 precursor STQVR.F heparin cofactor 2 P05546 (HEP2_HUMAN) K.YEITTIHNLFR.K 0.62 precursor heparin cofactor 2 P05546 (HEP2_HUMAN) R.LNILNAK.F 0.68 precursor heparin cofactor 2 P05546 (HEP2_HUMAN) R.NFGYTLR.S 0.64 precursor heparin cofactor 2 P05546 (HEP2_HUMAN) R.VLKDQVNTFDNIFIAPVGISTAMGM 0.63 precursor *ISLGLK.G hepatocyte cell Q14CZ8 (HECAM_HUMAN) K.PLLNDSRMLLSPDQK.V 0.61 adhesion molecule precursor hepatocyte growth Q04756 (HGFA_HUMAN) R.VQLSPDLLATLPEPASPGR.Q 0.82 factor activator preproprotein histidine-rich P04196 (HRG_HUMAN) R.DGYLFQLLR.I 0.63 glycoprotein precursor hyaluronan-binding Q14520 (HABP2_HUMAN) K.FLNWIK.A 0.82 protein 2 isoform 1 preproprotein hyaluronan-binding Q14520 (HABP2_HUMAN) K.LKPVDGHCALESK.Y 0.61 protein 2 isoform 1 preproprotein hyaluronan-binding Q14520 (HABP2_HUMAN) K.RPGVYTQVTK.F 0.74 protein 2 isoform 1 preproprotein inactive caspase-12 Q6UXS9 (CASPC_HUMAN) K.AGADTHGRLLQGNICNDAVTK.A 0.74 insulin-degrading P14735 (IDE_HUMAN) K.KIIEKM*ATFEIDEK.R 0.85 enzyme isoform 1 insulin-like growth P35858 (ALS_HUMAN) R.SFEGLGQLEVLTLDHNQLQEVK.A 0.62 factor-binding protein complex acid labile subunit isoform 2 precursor inter-alpha-trypsin P19827 (ITIH1_HUMAN) K.ELAAQTIKK.S 0.81 inhibitor heavy chain H1 isoform a precursor inter-alpha-trypsin P19827 (ITIH1_HUMAN) K.GSLVQASEANLQAAQDFVR.G 0.71 inhibitor heavy chain H1 isoform a precursor inter-alpha-trypsin P19827 (ITIH1_HUMAN) K.QLVHHFEIDVDIFEPQGISK.L 0.70 inhibitor heavy chain H1 isoform a precursor inter-alpha-trypsin P19827 (ITIH1_HUMAN) K.QYYEGSEIVVAGR.I 0.83 inhibitor heavy chain H1 isoform a precursor inter-alpha-trypsin P19827 (ITIH1_HUMAN) R.EVAFDLEIPKTAFISDFAVTADGNAFI 0.70 inhibitor heavy chain GDIK.D H1 isoform a precursor inter-alpha-trypsin P19827 (ITIH1_HUMAN) R.GMADQDGLKPTIDKPSEDSPPLEM* 0.63 inhibitor heavy chain LGPR.R H1 isoform a precursor inter-alpha-trypsin P19827 (ITIH1_HUMAN) R.GMADQDGLKPTIDKPSEDSPPLEML 0.60 inhibitor heavy chain GPR.R H1 isoform a precursor inter-alpha-trypsin P19823 (ITIH2_HUMAN) K.FDPAKLDQIESVITATSANTQLVLETL 0.80 inhibitor heavy chain AQM*DDLQDFLSK.D H2 precursor inter-alpha-trypsin P19823 (ITIH2_HUMAN) K.KFYNQVSTPLLR.N 0.76 inhibitor heavy chain H2 precursor inter-alpha-trypsin P19823 (ITIH2_HUMAN) K.NILFVIDVSGSM*WGVK.M 0.68 inhibitor heavy chain H2 precursor inter-alpha-trypsin P19823 (ITIH2_HUMAN) K.NILFVIDVSGSMWGVK.M 0.62 inhibitor heavy chain H2 precursor inter-alpha-trypsin P19823 (ITIH2_HUMAN) R.KLGSYEHR.I 0.72 inhibitor heavy chain H2 precursor inter-alpha-trypsin P19823 (ITIH2_HUMAN) R.LSNENHGIAQR.I 0.66 inhibitor heavy chain H2 precursor inter-alpha-trypsin P19823 (ITIH2_HUMAN) R.MATTMIQSK.V 0.60 inhibitor heavy chain H2 precursor inter-alpha-trypsin P19823 (ITIH2_HUMAN) R.SILQM*SLDHHIVTPLTSLVIENEAG 0.63 inhibitor heavy chain DER.M H2 precursor inter-alpha-trypsin P19823 (ITIH2_HUMAN) R.SILQMSLDHHIVTPLTSLVIENEAGDE 0.65 inhibitor heavy chain R.M H2 precursor inter-alpha-trypsin P19823 (ITIH2_HUMAN) R.TEVNVLPGAK.V 0.69 inhibitor heavy chain H2 precursor inter-alpha-trypsin Q14624 (ITIH4_HUMAN) K.NVVFVIDK.S 0.68 inhibitor heavy chain H4 isoform 1 precursor inter-alpha-trypsin Q14624 (ITIH4_HUMAN) K.WKETLFSVMPGLK.M 0.65 inhibitor heavy chain H4 isoform 1 precursor inter-alpha-trypsin Q14624 (ITIH4_HUMAN) K.YIFHNFM*ER.L 0.67 inhibitor heavy chain H4 isoform 1 precursor inter-alpha-trypsin Q14624 (ITIH4_HUMAN) R.FAHTVVTSR.V 0.63 inhibitor heavy chain H4 isoform 1 precursor inter-alpha-trypsin Q14624 (ITIH4_HUMAN) R.FKPTLSQQQK.S 0.60 inhibitor heavy chain H4 isoform 1 precursor inter-alpha-trypsin Q14624 (ITIH4_HUMAN) R.IHEDSDSALQLQDFYQEVANPLLTA 0.64 inhibitor heavy chain VTFEYPSNAVEEVTQNNFR.L H4 isoform 1 precursor inter-alpha-trypsin Q14624 (ITIH4_HUMAN) R.MNFRPGVLSSR.Q 0.63 inhibitor heavy chain H4 isoform 1 precursor inter-alpha-trypsin Q14624 (ITIH4_HUMAN) R.NVHSAGAAGSR.M 0.62 inhibitor heavy chain H4 isoform 1 precursor inter-alpha-trypsin Q14624 (ITIH4_HUMAN) R.NVHSGSTFFK.Y 0.75 inhibitor heavy chain H4 isoform 1 precursor inter-alpha-trypsin Q14624 (ITIH4_HUMAN) R.RLGVYELLLK.V 0.66 inhibitor heavy chain H4 isoform 1 precursor kallistatin precursor P29622 (KAIN_HUMAN) K.KLELHLPK.F 0.78 kallistatin precursor P29622 (KAIN_HUMAN) R.EIEEVLTPEMLMR.W 0.60 kininogen-1 isoform 2 P01042 (KNG1_HUMAN) K.AATGECTATVGKR.S 0.67 precursor kininogen-1 isoform 2 P01042 (KNG1_HUMAN) K.LGQSLDCNAEVYVVPWEK.K 0.72 precursor kininogen-1 isoform 2 P01042 (KNG1_HUMAN) K.YNSQNQSNNQFVLYR.I 0.62 precursor kininogen-1 isoform 2 P01042 (KNG1_HUMAN) R.QVVAGLNFR.I 0.64 precursor leucine-rich alpha-2- P02750 (A2GL_HUMAN) K.DLLLPQPDLR.Y 0.64 glycoprotein precursor leucine-rich alpha-2- P02750 (A2GL_HUMAN) R.LHLEGNKLQVLGK.D 0.76 glycoprotein precursor leucine-rich alpha-2- P02750 (A2GL_HUMAN) R.TLDLGENQLETLPPDLLR.G 0.61 glycoprotein precursor lipopolysaccharide- P18428 (LBP_HUMAN) K.GLQYAAQEGLLALQSELLR.I 0.82 binding protein precursor lipopolysaccharide- P18428 (LBP_HUMAN) K.LAEGFPLPLLK.R 0.66 binding protein precursor lumican precursor P51884 (LUM_HUMAN) K.SLEYLDLSFNQIAR.L 0.65 lumican precursor P51884 (LUM_HUMAN) R.LKEDAVSAAFK.G 0.74 m7GpppX Q96C86 (DCPS_HUMAN) R.IVFENPDPSDGFVLIPDLK.W 0.62 diphosphatase matrix Q99542 (MMP19_HUMAN) R.VYFFK.G 0.63 metalloproteinase-19 isoform 1 preproprotein MBT domain- Q05BQ5 (MBTD1_HUMAN) K.WFDYLR.E 0.65 containing protein 1 monocyte P08571 (CD14_HUMAN) R.LTVGAAQVPAQLLVGALR.V 0.66 differentiation antigen CD14 precursor pappalysin-1 Q13219 (PAPP1_HUMAN) R.VSFSSPLVAISGVALR.S 0.66 preproprotein phosphatidylinositol- P80108 (PHLD_HUMAN) K.GIVAAFYSGPSLSDKEK.L 0.71 glycan-specific phospholipase D precursor phosphatidylinositol- P80108 (PHLD_HUMAN) R.WYVPVKDLLGIYEK.L 0.71 glycan-specific phospholipase D precursor pigment epithelium- P36955 (PEDF_HUMAN) K.LQSLFDSPDFSK.I 0.61 derived factor precursor pigment epithelium- P36955 (PEDF_HUMAN) R.ALYYDLISSPDIHGTYK.E 0.72 derived factor precursor plasma kallikrein P03952 (KLKB1_HUMAN) R.CLLFSFLPASSINDMEKR.F 0.60 preproprotein plasma protease C1 P05155 (IC1_HUMAN) K.FQPTLLTLPR.I 0.70 inhibitor precursor plasma protease C1 P05155 (IC1_HUMAN) K.GVTSVSQIFHSPDLAIR.D 0.66 inhibitor precursor plasminogen isoform P00747 (PLMN_HUMAN) K.VIPACLPSPNYVVADR.T 0.63 1 precursor plasminogen isoform P00747 (PLMN_HUMAN) R.FVTWIEGVMR.N 0.60 1 precursor plasminogen isoform P00747 (PLMN_HUMAN) R.HSIFTPETNPR.A 0.63 1 precursor platelet basic protein P02775 (CXCL7_HUMAN) K.GKEESLDSDLYAELR.C 0.70 preproprotein platelet glycoprotein P40197 (GPV_HUMAN) K.MVLLEQLFLDHNALR.G 0.66 V precursor platelet glycoprotein P40197 (GPV_HUMAN) R.LVSLDSGLLNSLGALTELQFHR.N 0.88 V precursor pregnancy zone P20742 (PZP_HUMAN) K.ALLAYAFSLLGK.Q 0.66 protein precursor pregnancy zone P20742 (PZP_HUMAN) K.DLFHCVSFTLPR.I 0.86 protein precursor pregnancy zone P20742 (PZP_HUMAN) K.MLQITNTGFEMK.L 0.84 protein precursor pregnancy zone P20742 (PZP_HUMAN) R.NELIPLIYLENPRR.N 0.65 protein precursor pregnancy zone P20742 (PZP_HUMAN) R.SYIFIDEAHITQSLTWLSQMQK.D 0.68 protein precursor pregnancy-specific P11465 (PSG2_HUMAN) R.SDPVTLNLLHGPDLPR.I 0.66 beta-1-glycoprotein 2 precursor pregnancy-specific Q16557 (PSG3_HUMAN) R.TLFLFGVTK.Y 0.62 beta-1-glycoprotein 3 precursor pregnancy-specific Q15238 (PSG5_HUMAN) R.ILILPSVTR.N 0.76 beta-1-glycoprotein 5 precursor pregnancy-specific Q00889 (PSG6_HUMAN) R.SDPVTLNLLPK.L 0.63 beta-1-glycoprotein 6 isoform a progesterone- Q8WXW3 (PIBF1_HUMAN) R.VLQLEK.Q 0.71 induced-blocking factor 1 protein AMBP P02760 (AMBP_HUMAN) R.VVAQGVGIPEDSIFTMADR.G 0.60 preproprotein protein CBFA2T2 O43439 (MTG8R_HUMAN) R.LTEREWADEWKHLDHALNCIMEM 0.70 isoform MTGR1b VEK.T protein FAM98C Q17RN3 (FA98C_HUMAN) R.ALCGGDGAAALREPGAGLR.L 0.75 protein NLRC3 Q7RTR2 (NLRC3_HUMAN) K.ALM*DLLAGKGSQGSQAPQALDR.T 0.92 protein Z-dependent Q9UK55 (ZPI_HUMAN) K.MGDHLALEDYLTTDLVETWLR.N 0.60 protease inhibitor precursor prothrombin P00734 (THRB_HUMAN) K.SPQELLCGASLISDR.W 0.84 preproprotein prothrombin P00734 (THRB_HUMAN) R.LAVTTHGLPCLAWASAQAK.A 0.62 preproprotein prothrombin P00734 (THRB_HUMAN) R.SEGSSVNLSPPLEQCVPDR.G 0.70 preproprotein prothrombin P00734 (THRB_HUMAN) R.SGIECQLWR.S 0.68 preproprotein prothrombin P00734 (THRB_HUMAN) R.TATSEYQTFFNPR.T 0.60 preproprotein prothrombin P00734 (THRB_HUMAN) R.VTGWGNLKETWTANVGK.G 0.69 preproprotein putative Q5T013 (HYI_HUMAN) R.IHLM*AGR.V 0.69 hydroxypyruvate isomerase isoform 1 putative Q5T013 (HYI_HUMAN) R.IHLMAGR.V 0.66 hydroxypyruvate isomerase isoform 1 ras-like protein family Q92737 (RSLAA_HUMAN) R.PAHPALR.L 0.71 member 10A precursor ras-related GTP- Q7L523 (RRAGA_HUMAN) K.ISNIIK.Q 0.82 binding protein A retinol-binding P02753 (RET4_HUMAN) K.M*KYWGVASFLQK.G 0.73 protein 4 precursor retinol-binding P02753 (RET4_HUMAN) R.FSGTWYAM*AK.K 0.63 protein 4 precursor retinol-binding P02753 (RET4_HUMAN) R.LLNLDGTCADSYSFVFSR.D 0.79 protein 4 precursor retinol-binding P02753 (RET4_HUMAN) R.LLNNWDVCADMVGTFTDTEDPAKF 0.77 protein 4 precursor K.M sex hormone-binding P04278 (SHBG_HUMAN) R.LFLGALPGEDSSTSFCLNGLWAQGQ 0.66 globulin isoform 1 R.L precursor sex hormone-binding P04278 (SHBG_HUMAN) K.DDWFMLGLR.D 0.60 globulin isoform 4 precursor sex hormone-binding P04278 (SHBG_HUMAN) R.SCDVESNPGIFLPPGTQAEFNLR.G 0.64 globulin isoform 4 precursor sex hormone-binding P04278 (SHBG_HUMAN) R.TWDPEGVIFYGDTNPKDDWFM*L 0.65 globulin isoform 4 GLR.D precursor sex hormone-binding P04278 (SHBG_HUMAN) R.TWDPEGVIFYGDTNPKDDWFMLGL 0.66 globulin isoform 4 R.D precursor signal transducer and P52630 (STAT2_HUMAN) R.KFCRDIQDPTQLAEMIFNLLLEEK.R 0.73 activator of transcription 2 spectrin beta chain, Q13813 (SPTN1_HUMAN) R.NELIRQEKLEQLAR.R 0.60 non-erythrocytic 1 stabilin-1 precursor Q9NY15 (STAB1_HUMAN) R.KNLSER.W 0.88 succinate- P51649 (SSDH_HUMAN) R.KWYNLMIQNK.D 0.88 semialdehyde dehydrogenase, mitochondrial tetranectin precursor P05452 (TETN_HUMAN) K.SRLDTLAQEVALLK.E 0.75 THAP domain- Q8TBB0 (THAP6_HUMAN) K.RLDVNAAGIWEPKK.G 0.69 containing protein 6 thyroxine-binding P05543 (THBG_HUMAN) R.SILFLGK.V 0.79 globulin precursor tripartite motif- Q9C035 (TRIM5_HUMAN) R.ELISDLEHRLQGSVM*ELLQGVDGVI 0.60 containing protein 5 K.R vitamin D-binding P02774 (VTDB_HUMAN) K.EDFTSLSLVLYSR.K 0.66 protein isoform 1 precursor vitamin D-binding P02774 (VTDB_HUMAN) K.ELSSFIDKGQELCADYSENTFTEYK.K 0.67 protein isoform 1 precursor vitamin D-binding P02774 (VTDB_HUMAN) K.ELSSFIDKGQELCADYSENTFTEYKK.K 0.66 protein isoform 1 precursor vitamin D-binding P02774 (VTDB_HUMAN) K.EVVSLTEACCAEGADPDCYDTR.T 0.65 protein isoform 1 precursor vitamin D-binding P02774 (VTDB_HUMAN) K.TAMDVFVCTYFMPAAQLPELPDVEL 0.84 protein isoform 1 PTNKDVCDPGNTK.V precursor vitamin D-binding P02774 (VTDB_HUMAN) R.RTHLPEVFLSK.V 0.69 protein isoform 1 precursor vitamin D-binding P02774 (VTDB_HUMAN) R.VCSQYAAYGEK.K 0.66 protein isoform 1 precursor vitronectin precursor P04004 (VTNC_HUMAN) K.LIRDVWGIEGPIDAAFTR.I 0.61 vitronectin precursor P04004 (VTNC_HUMAN) R.DVWGIEGPIDAAFTR.I 0.63 vitronectin precursor P04004 (VTNC_HUMAN) R.ERVYFFK.G 0.81 vitronectin precursor P04004 (VTNC_HUMAN) R.FEDGVLDPDYPR.N 0.64 vitronectin precursor P04004 (VTNC_HUMAN) R.IYISGM*APRPSLAK.K 0.75 zinc finger protein P52746 (ZN142_HUMAN) K.TRFLLR.T 0.66 142

TABLE 9 Significant peptides (AUC > 0.6) for for X!Tandem only Protein description Uniprot ID (name) Peptide XT_AUC afamin precursor P43652 K.HELTDEELQSLFTNFANVVDK.C 0.65 (AFAM_HUMAN) afamin precursor P43652 R.NPFVFAPTLLTVAVHFEEVAK.S 0.91 (AFAM_HUMAN) alpha-1- P01011 K.ADLSGITGAR.N 0.67 antichymotrypsin (AACT_HUMAN) precursor alpha-1- P01011 K.MEEVEAMLLPETLKR.W 0.60 antichymotrypsin (AACT_HUMAN) precursor alpha-1- P01011 K.WEMPFDPQDTHQSR.F 0.64 antichymotrypsin (AACT_HUMAN) precursor alpha-1- P01011 R.LYGSEAFATDFQDSAAAK.K 0.62 antichymotrypsin (AACT_HUMAN) precursor alpha-1B-glycoprotein P04217 K.HQFLLTGDTQGR.Y 0.72 precursor (A1BG_HUMAN) alpha-1B-glycoprotein P04217 K.NGVAQEPVHLDSPAIK.H 0.63 precursor (A1BG_HUMAN) alpha-1B-glycoprotein P04217 K.SLPAPWLSM*APVSWITPGLK.T 0.72 precursor (A1BG_HUMAN) alpha-1B-glycoprotein P04217 K.VTLTCVAPLSGVDFQLRR.G 0.67 precursor (A1BG_HUMAN) alpha-1B-glycoprotein P04217 R.C*EGPIPDVTFELLR.E 0.67 precursor (A1BG_HUMAN) alpha-1B-glycoprotein P04217 R.C*LAPLEGAR.F 0.79 precursor (A1BG_HUMAN) alpha-1B-glycoprotein P04217 R.CLAPLEGAR.F 0.63 precursor (A1BG_HUMAN) alpha-1B-glycoprotein P04217 R.GVTFLLR.R 0.69 precursor (A1BG_HUMAN) alpha-1B-glycoprotein P04217 R.LHDNQNGWSGDSAPVELILSDETL 0.60 precursor (A1BG_HUMAN) PAPEFSPEPESGR.A alpha-1B-glycoprotein P04217 R.TPGAAANLELIFVGPQHAGNYR.C 0.62 precursor (A1BG_HUMAN) alpha-2-antiplasmin P08697 K.HQM*DLVATLSQLGLQELFQAPDL 0.61 isoform a precursor (A2AP_HUMAN) R.G alpha-2-antiplasmin P08697 R.LCQDLGPGAFR.L 0.68 isoform a precursor (A2AP_HUMAN) alpha-2-antiplasmin P08697 R.WFLLEQPEIQVAHFPFK.N 0.60 isoform a precursor (A2AP_HUMAN) alpha-2-HS- P02765 K.VWPQQPSGELFEIEIDTLETTCHVL 0.61 glycoprotein (FETUA_HUMAN) DPTPVAR.C preproprotein alpha-2-HS- P02765 R.HTFMGVVSLGSPSGEVSHPR.K 0.68 glycoprotein (FETUA_HUMAN) preproprotein alpha-2-HS- P02765 R.Q*PNCDDPETEEAALVAIDYINQNL 0.69 glycoprotein (FETUA_HUMAN) PWGYK.H preproprotein alpha-2-HS- P02765 R.QPNCDDPETEEAALVAIDYINQNLP 0.64 glycoprotein (FETUA_HUMAN) WGYK.H preproprotein alpha-2-HS- P02765 R.TVVQPSVGAAAGPVVPPCPGR.I 0.64 glycoprotein (FETUA_HUMAN) preproprotein angiotensinogen P01019 K.QPFVQGLALYTPVVLPR.S 0.73 preproprotein (ANGT_HUMAN) angiotensinogen P01019 R.AAM*VGM*LANFLGFR.I 0.62 preproprotein (ANGT_HUMAN) apolipoprotein A-IV P06727 K.LVPFATELHER.L 0.64 precursor (APOA4_HUMAN) apolipoprotein A-IV P06727 R.LLPHANEVSQK.I 0.61 precursor (APOA4_HUMAN) apolipoprotein A-IV P06727 R.SLAPYAQDTQEKLNHQLEGLTFQM 0.70 precursor (APOA4_HUMAN) K.K apolipoprotein B-100 P04114 K.FPEVDVLTK.Y 0.61 precursor (APOB_HUMAN) apolipoprotein B-100 P04114 K.HINIDQFVR.K 0.70 precursor (APOB_HUMAN) apolipoprotein B-100 P04114 K.LLSGGNTLHLVSTTK.T 0.66 precursor (APOB_HUMAN) apolipoprotein B-100 P04114 K.Q*VFLYPEKDEPTYILNIKR.G 0.81 precursor (APOB_HUMAN) apolipoprotein B-100 P04114 K.QVFLYPEKDEPTYILNIKR.G 0.77 precursor (APOB_HUMAN) apolipoprotein B-100 P04114 K.SLHMYANR.L 0.83 precursor (APOB_HUMAN) apolipoprotein B-100 P04114 K.SVSDGIAALDLNAVANK.I 0.62 precursor (APOB_HUMAN) apolipoprotein B-100 P04114 K.SVSLPSLDPASAKIEGNLIFDPNNYL 0.67 precursor (APOB_HUMAN) PK.E apolipoprotein B-100 P04114 K.TEVIPPLIENR.Q 0.63 precursor (APOB_HUMAN) apolipoprotein B-100 P04114 K.VLVDHFGYTK.D 0.76 precursor (APOB_HUMAN) apolipoprotein B-100 P04114 R.TSSFALNLPTLPEVKFPEVDVLTK.Y 0.62 precursor (APOB_HUMAN) apolipoprotein C-III P02656 R.GWVTDGFSSLKDYWSTVK.D 0.66 precursor (APOC3_HUMAN) apolipoprotein E P02649 R.GEVQAMLGQSTEELR.V 0.81 precursor (APOE_HUMAN) apolipoprotein E P02649 R.LAVYQAGAR.E 0.63 precursor (APOE_HUMAN) apolipoprotein E P02649 R.LGPLVEQGR.V 0.69 precursor (APOE_HUMAN) attractin isoform 2 O75882 K.LTLTPWVGLR.K 0.69 preproprotein (ATRN_HUMAN) beta-2-glycoprotein 1 P02749 K.FICPLTGLWPINTLK.C 0.63 precursor (APOH_HUMAN) beta-2-glycoprotein 1 P02749 K.TFYEPGEEITYSCKPGYVSR.G 0.62 precursor (APOH_HUMAN) beta-Ala-His Q96KN2 K.MVVSMTLGLHPWIANIDDTQYLA 0.81 dipeptidase precursor (CNDP1_HUMAN) AK.R beta-Ala-His Q96KN2 K.VFQYIDLHQDEFVQTLK.E 0.65 dipeptidase precursor (CNDP1_HUMAN) biotinidase precursor P43251 R.TSIYPFLDFM*PSPQVVR.W 0.79 (BTD_HUMAN) carboxypeptidase N P15169 R.ELMLQLSEFLCEEFR.N 0.61 catalytic chain (CBPN_HUMAN) precursor ceruloplasmin P00450 K.AEEEHLGILGPQLHADVGDKVK.I 0.73 precursor (CERU_HUMAN) ceruloplasmin P00450 K.ALYLQYTDETFR.T 0.64 precursor (CERU_HUMAN) ceruloplasmin P00450 K.DVDKEFYLFPTVFDENESLLLEDNIR 0.62 precursor (CERU_HUMAN) .M ceruloplasmin P00450 K.HYYIGIIETTWDYASDHGEK.K 0.61 precursor (CERU_HUMAN) ceruloplasmin P00450 R.EYTDASFTNRK.E 0.67 precursor (CERU_HUMAN) ceruloplasmin P00450 R.HYYIAAEEIIWNYAPSGIDIFTK.E 0.63 precursor (CERU_HUMAN) ceruloplasmin P00450 R.IYHSHIDAPK.D 0.62 precursor (CERU_HUMAN) ceruloplasmin P00450 R.Q*KDVDKEFYLFPTVFDENESLLLE 0.74 precursor (CERU_HUMAN) DNIR.M ceruloplasmin P00450 R.QKDVDKEFYLFPTVFDENESLLLED 0.65 precursor (CERU_HUMAN) NIR.M ceruloplasmin P00450 R.TYYIAAVEVEWDYSPQR.E 0.90 precursor (CERU_HUMAN) coagulation factor IX P00740 R.SALVLQYLR.V 0.69 preproprotein (FA9_HUMAN) coagulation factor V P12259 K.EFNPLVIVGLSK.D 0.61 precursor (FA5_HUMAN) coagulation factor XII P00748 R.NPDNDIRPWCFVLNR.D 0.65 precursor (FA12_HUMAN) coagulation factor XII P00748 R.VVGGLVALR.G 0.61 precursor (FA12_HUMAN) complement C1q P02746 K.NSLLGMEGANSIFSGFLLFPDMEA.- 0.64 subcomponent subunit (C1QB_HUMAN) B precursor complement C1q P02746 K.VPGLYYFTYHASSR.G 0.63 subcomponent subunit (C1QB_HUMAN) B precursor complement C1q P02747 R.Q*THQPPAPNSLIR.F 0.60 subcomponent subunit (C1QC_HUMAN) C precursor complement C1r P00736 R.LPVANPQACENWLR.G 0.72 subcomponent (C1R_HUMAN) precursor complement C2 P06681 K.NQGILEFYGDDIALLK.L 0.74 isoform 3 (CO2_HUMAN) complement C2 P06681 K.RNDYLDIYAIGVGK.L 0.61 isoform 3 (CO2_HUMAN) complement C2 P06681 R.QPYSYDFPEDVAPALGTSFSHMLG 0.78 isoform 3 (CO2_HUMAN) ATNPTQK.T complement C3 P01024 R.IHWESASLLR.S 0.69 precursor (CO3_HUMAN) complement C4-A P0C0L4 K.FACYYPR.V 0.64 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 K.LHLETDSLALVALGALDTALYAAGS 0.74 isoform 1 (CO4A_HUMAN) K.S complement C4-A P0C0L4 K.LVNGQSHISLSK.A 0.64 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 K.M*RPSTDTITVMVENSHGLR.V 0.60 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 K.MRPSTDTITVMVENSHGLR.V 0.65 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 K.SCGLHQLLR.G 0.74 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 K.VGLSGMAIADVTLLSGFHALR.A 0.61 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 K.YVLPNFEVK.I 0.64 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 R.ALEILQEEDLIDEDDIPVR.S 0.64 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 R.ECVGFEAVQEVPVGLVQPASATLY 0.62 isoform 1 (CO4A_HUMAN) DYYNPER.R complement C4-A P0C0L4 R.EELVYELNPLDHR.G 0.66 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 R.STQDTVIALDALSAYWIASHTTEER.G 0.70 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 R.VGDTLNLNLR.A 0.79 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 R.VHYTVCIWR.N 0.65 isoform 1 (CO4A_HUMAN) complement C4-B-like P0C0L5 K.GLCVATPVQLR.V 1.00 preproprotein (CO4B_HUMAN) complement C4-B-like P0C0L5 K.KYVLPNFEVK.I 0.60 preproprotein (CO4B_HUMAN) complement C4-B-like P0C0L5 K.VDFTLSSERDFALLSLQVPLKDAK.S 0.74 preproprotein (CO4B_HUMAN) complement C4-B-like P0C0L5 R.EMSGSPASGIPVK.V 0.72 preproprotein (CO4B_HUMAN) complement C4-B-like P0C0L5 R.GCGEQTM*IYLAPTLAASR.Y 0.75 preproprotein (CO4B_HUMAN) complement C4-B-like P0C0L5 R.NGESVKLHLETDSLALVALGALDTA 0.85 preproprotein (CO4B_HUMAN) LYAAGSK.S complement C5 P01031 R.IPLDLVPK.T 0.65 preproprotein (CO5_HUMAN) complement C5 P01031 R.SYFPESWLWEVHLVPR.R 0.63 preproprotein (CO5_HUMAN) complement C5 P01031 R.YGGGFYSTQDTINAIEGLTEYSLLVK 0.62 preproprotein (CO5_HUMAN) .Q complement P13671 K.ENPAVIDFELAPIVDLVR.N 0.63 component C6 (CO6_HUMAN) precursor complement P07357 K.YNPVVIDFEMQPIHEVLR.H 0.61 component C8 alpha (CO8A_HUMAN) chain precursor complement P07357 R.HTSLGPLEAK.R 0.65 component C8 alpha (CO8A_HUMAN) chain precursor complement P07358 K.C*QHEMDQYWGIGSLASGINLFTN 0.61 component C8 beta (CO8B_HUMAN) SFEGPVLDHR.Y chain preproprotein complement P07358 K.SGFSFGFK.I 0.64 component C8 beta (CO8B_HUMAN) chain preproprotein complement P07358 R.DTMVEDLVVLVR.G 0.77 component C8 beta (CO8B_HUMAN) chain preproprotein complement P07360 K.ANFDAQQFAGTWLLVAVGSACR.F 0.63 component C8 gamma (CO8G_HUMAN) chain precursor complement P07360 R.AEATTLHVAPQGTAMAVSTFR.K 0.61 component C8 gamma (CO8G_HUMAN) chain precursor complement P02748 R.DVVLTTTFVDDIK.A 0.73 component C9 (CO9_HUMAN) precursor complement P02748 R.RPWNVASLIYETK.G 0.66 component C9 (CO9_HUMAN) precursor complement factor B P00751 K.ISVIRPSK.G 0.70 preproprotein (CFAB_HUMAN) complement factor B P00751 K.VASYGVKPR.Y 0.63 preproprotein (CFAB_HUMAN) complement factor B P00751 R.DFHINLFQVLPWLK.E 0.68 preproprotein (CFAB_HUMAN) complement factor B P00751 R.DLLYIGK.D 0.63 preproprotein (CFAB_HUMAN) complement factor B P00751 R.GDSGGPLIVHK.R 0.63 preproprotein (CFAB_HUMAN) complement factor B P00751 R.LEDSVTYHCSR.G 0.68 preproprotein (CFAB_HUMAN) complement factor B P00751 R.LPPTTTCQQQK.E 0.68 preproprotein (CFAB_HUMAN) complement factor H P08603 K.CLHPCVISR.E 0.62 isoform a precursor (CFAH_HUMAN) complement factor H P08603 K.CTSTGWIPAPR.C 0.74 isoform a precursor (CFAH_HUMAN) complement factor H P08603 K.IDVHLVPDR.K 0.66 isoform a precursor (CFAH_HUMAN) complement factor H P08603 K.IVSSAMEPDREYHFGQAVR.F 0.67 isoform a precursor (CFAH_HUMAN) complement factor H P08603 K.SIDVACHPGYALPK.A 0.67 isoform a precursor (CFAH_HUMAN) complement factor H P08603 K.VSVLCQENYLIQEGEEITCKDGR.W 0.63 isoform a precursor (CFAH_HUMAN) complement factor H P08603 K.WSSPPQCEGLPCK.S 0.60 isoform a precursor (CFAH_HUMAN) complement factor H P08603 R.EIMENYNIALR.W 0.61 isoform a precursor (CFAH_HUMAN) complement factor H P08603 R.RPYFPVAVGK.Y 0.83 isoform a precursor (CFAH_HUMAN) complement factor H P08603 R.WQSIPLCVEK.I 0.63 isoform a precursor (CFAH_HUMAN) complement factor I P05156 R.YQIWTTVVDWIHPDLKR.I 0.72 preproprotein (CFAI_HUMAN) corticosteroid-binding P08185 K.AVLQLNEEGVDTAGSTGVTLNLTSK 0.61 globulin precursor (CBG_HUMAN) PIILR.F corticosteroid-binding P08185 R.GLASANVDFAFSLYK.H 0.66 globulin precursor (CBG_HUMAN) fibrinogen alpha chain P02671 K.TFPGFFSPMLGEFVSETESR.G 0.62 isoform alpha-E (FIBA_HUMAN) preproprotein gelsolin isoform b P06396 K.FDLVPVPTNLYGDFFTGDAYVILK.T 0.66 (GELS_HUMAN) gelsolin isoform b P06396 K.QTQVSVLPEGGETPLFK.Q 0.66 (GELS_HUMAN) gelsolin isoform b P06396 K.TPSAAYLWVGTGASEAEK.T 0.71 (GELS_HUMAN) gelsolin isoform b P06396 R.AQPVQVAEGSEPDGFWEALGGK.A 0.67 (GELS_HUMAN) gelsolin isoform b P06396 R.IEGSNKVPVDPATYGQFYGGDSYIIL 0.60 (GELS_HUMAN) YNYR.H gelsolin isoform b P06396 R.VEKFDLVPVPTNLYGDFFTGDAYVI 0.73 (GELS_HUMAN) LK.T gelsolin isoform b P06396 R.VPFDAATLHTSTAMAAQHGMDD 0.63 (GELS_HUMAN) DGTGQK.Q glutathione peroxidase P22352 K.FLVGPDGIPIMR.W 0.60 3 precursor (GPX3_HUMAN) hemopexin precursor P02790 K.ALPQPQNVTSLLGCTH.- 0.63 (HEMO_HUMAN) hemopexin precursor P02790 K.SLGPNSCSANGPGLYLIHGPNLYCY 0.68 (HEMO_HUMAN) SDVEK.L hemopexin precursor P02790 R.DGWHSWPIAHQWPQGPSAVDAA 0.63 (HEMO_HUMAN) FSWEEK.L hemopexin precursor P02790 R.GECQAEGVLFFQGDR.E 0.67 (HEMO_HUMAN) hemopexin precursor P02790 R.GECQAEGVLFFQGDREWFWDLAT 0.67 (HEMO_HUMAN) GTM*K.E hemopexin precursor P02790 R.LEKEVGTPHGIILDSVDAAFICPGSS 0.75 (HEMO_HUMAN) R.L hemopexin precursor P02790 R.LWWLDLK.S 0.62 (HEMO_HUMAN) hemopexin precursor P02790 R.WKNFPSPVDAAFR.Q 0.68 (HEMO_HUMAN) heparin cofactor 2 P05546 K.DQVNTFDNIFIAPVGISTAMGMISL 0.60 precursor (HEP2_HUMAN) GLK.G insulin-like growth P35858 K.ANVFVQLPR.L 0.71 factor-binding protein (ALS_HUMAN) complex acid labile subunit isoform 2 precursor insulin-like growth P35858 R.LEALPNSLLAPLGR.L 0.61 factor-binding protein (ALS_HUMAN) complex acid labile subunit isoform 2 precursor insulin-like growth P35858 R.LFQGLGK.L 0.68 factor-binding protein (ALS_HUMAN) complex acid labile subunit isoform 2 precursor insulin-like growth P35858 R.NLIAAVAPGAFLGLK.A 0.76 factor-binding protein (ALS_HUMAN) complex acid labile subunit isoform 2 precursor insulin-like growth P35858 R.TFTPQPPGLER.L 0.73 factor-binding protein (ALS_HUMAN) complex acid labile subunit isoform 2 precursor inter-alpha-trypsin P19827 K.Q*LVHHFEIDVDIFEPQGISK.L 0.69 inhibitor heavy chain (ITIH1_HUMAN) H1 isoform a precursor inter-alpha-trypsin P19827 K.VTFQLTYEEVLK.R 0.61 inhibitor heavy chain (ITIH1_HUMAN) H1 isoform a precursor inter-alpha-trypsin P19827 K.VTFQLTYEEVLKR.N 0.70 inhibitor heavy chain (ITIH1_HUMAN) H1 isoform a precursor inter-alpha-trypsin P19827 R.GIEILNQVQESLPELSNHASILIMLT 0.62 inhibitor heavy chain (ITIH1_HUMAN) DGDPTEGVTDR.S H1 isoform a precursor inter-alpha-trypsin P19827 R.GM*ADQDGLKPTIDKPSEDSPPLE 0.79 inhibitor heavy chain (ITIH1_HUMAN) M*LGPR.R H1 isoform a precursor inter-alpha-trypsin P19827 R.KAAISGENAGLVR.A 0.78 inhibitor heavy chain (ITIH1_HUMAN) H1 isoform a precursor inter-alpha-trypsin P19823 K.AGELEVFNGYFVHFFAPDNLDPIPK 0.64 inhibitor heavy chain (ITIH2_HUMAN) .N H2 precursor inter-alpha-trypsin P19823 K.FYNQVSTPLLR.N 0.68 inhibitor heavy chain (ITIH2_HUMAN) H2 precursor inter-alpha-trypsin P19823 K.VQFELHYQEVK.W 0.68 inhibitor heavy chain (ITIH2_HUMAN) H2 precursor inter-alpha-trypsin P19823 R.ETAVDGELVVLYDVK.R 0.63 inhibitor heavy chain (ITIH2_HUMAN) H2 precursor inter-alpha-trypsin P19823 R.IYLQPGR.L 0.75 inhibitor heavy chain (ITIH2_HUMAN) H2 precursor inter-alpha-trypsin Q06033 R.LWAYLTIEQLLEK.R 0.60 inhibitor heavy chain (ITIH3_HUMAN) H3 preproprotein inter-alpha-trypsin Q14624 K.ITFELVYEELLK.R 0.60 inhibitor heavy chain (ITIH4_HUMAN) H4 isoform 1 precursor inter-alpha-trypsin Q14624 K.LQDRGPDVLTATVSGK.L 0.67 inhibitor heavy chain (ITIH4_HUMAN) H4 isoform 1 precursor inter-alpha-trypsin Q14624 K.TGLLLLSDPDKVTIGLLFWDGRGEG 0.63 inhibitor heavy chain (ITIH4_HUMAN) LR.L H4 isoform 1 precursor inter-alpha-trypsin Q14624 K.WKETLFSVM*PGLK.M 0.79 inhibitor heavy chain (ITIH4_HUMAN) H4 isoform 1 precursor inter-alpha-trypsin Q14624 R.AISGGSIQIENGYFVHYFAPEGLTT 0.60 inhibitor heavy chain (ITIH4_HUMAN) M*PK.N H4 isoform 1 precursor inter-alpha-trypsin Q14624 R.AISGGSIQIENGYFVHYFAPEGLTT 0.65 inhibitor heavy chain (ITIH4_HUMAN) MPK.N H4 isoform 1 precursor inter-alpha-trypsin Q14624 R.ANTVQEATFQMELPK.K 0.68 inhibitor heavy chain (ITIH4_HUMAN) H4 isoform 1 precursor inter-alpha-trypsin Q14624 R.SFAAGIQALGGTNINDAMLMAVQ 0.64 inhibitor heavy chain (ITIH4_HUMAN) LLDSSNQEER.L H4 isoform 1 precursor inter-alpha-trypsin Q14624 R.VQGNDHSATR.E 0.63 inhibitor heavy chain (ITIH4_HUMAN) H4 isoform 1 precursor inter-alpha-trypsin Q14624 K.ITFELVYEELLKR.R 0.60 inhibitor heavy chain (ITIH4_HUMAN) H4 isoform 2 precursor inter-alpha-trypsin Q14624 K.VTIGLLFWDGR.G 0.65 inhibitor heavy chain (ITIH4_HUMAN) H4 isoform 2 precursor inter-alpha-trypsin Q14624 R.LWAYLTIQQLLEQTVSASDADQQA 0.68 inhibitor heavy chain (ITIH4_HUMAN) LR.N H4 isoform 2 precursor kallistatin precursor P29622 K.LFHTNFYDTVGTIQLINDHVK.K 0.73 (KAIN_HUMAN) kininogen-1 isoform 2 P01042 K.ENFLFLTPDCK.S 0.64 precursor (KNG1_HUMAN) kininogen-1 isoform 2 P01042 K.IYPTVNCQPLGMISLMK.R 0.64 precursor (KNG1_HUMAN) kininogen-1 isoform 2 P01042 K.KIYPTVNCQPLGMISLMK.R 0.78 precursor (KNG1_HUMAN) kininogen-1 isoform 2 P01042 K.SLWNGDTGECTDNAYIDIQLR.I 0.67 precursor (KNG1_HUMAN) lumican precursor P51884 K.ILGPLSYSK.I 0.60 (LUM_HUMAN) N-acetylmuramoyl-L- Q96PD5 K.EYGVVLAPDGSTVAVEPLLAGLEAG 0.61 alanine amidase (PGRP2_HUMAN) LQGR.R precursor N-acetylmuramoyl-L- Q96PD5 R.EGKEYGVVLAPDGSTVAVEPLLAGL 0.69 alanine amidase (PGRP2_HUMAN) EAGLQGR.R precursor N-acetylmuramoyl-L- Q96PD5 R.Q*NGAALTSASILAQQVWGTLVLL 0.60 alanine amidase (PGRP2_HUMAN) QR.L precursor pigment epithelium- P36955 K.IAQLPLTGSMSIIFFLPLK.V 0.65 derived factor (PEDF_HUMAN) precursor pigment epithelium- P36955 R.SSTSPTTNVLLSPLSVATALSALSLG 0.79 derived factor (PEDF_HUMAN) AEQR.T precursor plasma kallikrein P03952 K.VAEYMDWILEK.T 0.62 preproprotein (KLKB1_HUMAN) plasma kallikrein P03952 R.C*LLFSFLPASSINDMEKR.F 0.60 preproprotein (KLKB1_HUMAN) plasma kallikrein P03952 R.C*QFFSYATQTFHK.A 0.60 preproprotein (KLKB1_HUMAN) plasma kallikrein P03952 R.CLLFSFLPASSINDMEK.R 0.76 preproprotein (KLKB1_HUMAN) plasma protease C1 P05155 R.LVLLNAIYLSAK.W 0.96 inhibitor precursor (IC1_HUMAN) pregnancy zone protein P20742 R.NALFCLESAWNVAK.E 0.67 precursor (PZP_HUMAN) pregnancy zone protein P20742 R.NQGNTWLTAFVLK.T 0.61 precursor (PZP_HUMAN) pregnancy-specific Q00887 R.SNPVILNVLYGPDLPR.I 0.62 beta-1-glycoprotein 9 (PSG9_HUMAN) precursor prenylcysteine oxidase Q9UHG3 K.IAIIGAGIGGTSAAYYLR.Q 0.71 1 precursor (PCYOX_HUMAN) protein AMBP P02760 K.WYNLAIGSTCPWLK.K 0.77 preproprotein (AMBP_HUMAN) protein AMBP P02760 R.TVAACNLPIVR.G 0.66 preproprotein (AMBP_HUMAN) prothrombin P00734 .R.IVEGSDAEIGMSPWQVMLFR.K 0.62 preproprotein (THRB_HUMAN) prothrombin P00734 R.RQECSIPVCGQDQVTVAMTPR.S 0.69 preproprotein (THRB_HUMAN) prothrombin P00734 R.TFGSGEADCGLRPLFEK.K 0.61 preproprotein (THRB_HUMAN) retinol-binding protein P02753 R.FSGTWYAMAK.K 0.60 4 precursor (RET4_HUMAN) retinol-binding protein P02753 R.LLNNWDVCADMVGTFTDTEDPAK 0.64 4 precursor (RET4_HUMAN) .F serum amyloid P- P02743 R.GYVIIKPLVWV.- 0.62 component precursor (SAMP_HUMAN) sex hormone-binding P04278 K.VVLSSGSGPGLDLPLVLGLPLQLK.L 0.60 globulin isoform 1 (SHBG_HUMAN) precursor sex hormone-binding P04278 R.TWDPEGVIFYGDTNPKDDWFM*L 0.75 globulin isoform 1 (SHBG_HUMAN) GLR.D precursor sex hormone-binding P04278 R.TWDPEGVIFYGDTNPKDDWFMLG 0.74 globulin isoform 1 (SHBG_HUMAN) LR.D precursor thrombospondin-1 P07996 K.GFLLLASLR.Q 0.70 precursor (TSP1_HUMAN) thyroxine-binding P05543 K.AVLHIGEK.G 0.85 globulin precursor (THBG_HUMAN) thyroxine-binding P05543 K.FSISATYDLGATLLK.M 0.65 globulin precursor (THBG_HUMAN) thyroxine-binding P05543 K.KELELQIGNALFIGK.H 0.61 globulin precursor (THBG_HUMAN) thyroxine-binding P05543 K.MSSINADFAFNLYR.R 0.67 globulin precursor (THBG_HUMAN) transforming growth Q15582 R.LTLLAPLNSVFK.D 0.65 factor-beta-induced (BGH3_HUMAN) protein ig-h3 precursor transthyretin precursor P02766 R.GSPAINVAVHVFR.K 0.67 (TTHY_HUMAN) uncharacterized Q8ND61 K.MPSHLMLAR.K 0.64 protein C3orf20 (CC020_HUMAN) isoform 1 vitamin D-binding P02774 K.ELPEHTVK.L 0.75 protein isoform 1 (VTDB_HUMAN) precursor vitamin D-binding P02774 K.EYANQFMWEYSTNYGQAPLSLLVS 0.69 protein isoform 1 (VTDB_HUMAN) YTK.S precursor vitamin D-binding P02774 K.HLSLLTTLSNR.V 0.65 protein isoform 1 (VTDB_HUMAN) precursor vitamin D-binding P02774 K.HQPQEFPTYVEPTNDEICEAFR.K 0.64 protein isoform 1 (VTDB_HUMAN) precursor vitamin D-binding P02774 K.LAQKVPTADLEDVLPLAEDITNILSK.C 0.73 protein isoform 1 (VTDB_HUMAN) precursor vitamin D-binding P02774 K.LCDNLSTK.N 0.70 protein isoform 1 (VTDB_HUMAN) precursor vitamin D-binding P02774 K.LCMAALK.H 0.63 protein isoform 1 (VTDB_HUMAN) precursor vitamin D-binding P02774 K.SCESNSPFPVHPGTAECCTK.E 0.63 protein isoform 1 (VTDB_HUMAN) precursor vitamin D-binding P02774 K.SYLSMVGSCCTSASPTVCFLK.E 0.61 protein isoform 1 (VTDB_HUMAN) precursor vitamin D-binding P02774 K.TAMDVFVCTYFM*PAAQLPELPDV 0.61 protein isoform 1 (VTDB_HUMAN) ELPTNK.D precursor vitamin D-binding P02774 K.VLEPTLK.S 0.69 protein isoform 1 (VTDB_HUMAN) precursor vitamin D-binding P02774 R.KFPSGTFEQVSQLVK.E 0.66 protein isoform 1 (VTDB_HUMAN) precursor vitamin D-binding P02774 R.THLPEVFLSK.V 0.62 protein isoform 1 (VTDB_HUMAN) precursor vitamin D-binding P02774 R.TSALSAK.S 0.74 protein isoform 1 (VTDB_HUMAN) precursor vitronectin precursor P04004 R.GQYCYELDEK.A 0.73 (VTNC_HUMAN) vitronectin precursor P04004 R.M*DWLVPATCEPIQSVFFFSGDK.Y 0.64 (VTNC_HUMAN) vitronectin precursor P04004 R.Q*PQFISR.D 0.63 (VTNC_HUMAN)

TABLE 10 Significant peptides (AUC > 0.6) for both X!Tandem and Sequest Protein description Uniprot ID (name) Peptide XT_AUC S_AUC afamin precursor P43652 K.HFQNLGK.D 0.74 0.61 (AFAM_HUMAN) afamin precursor P43652 R.RHPDLSIPELL 0.67 0.63 (AFAM_HUMAN) R.I afamin precursor P43652 R.TINPAVDHCC 0.66 0.86 (AFAM_HUMAN) K.T alpha-1-antichymotrypsin P01011 K.ITDLIKDLDSQ 0.71 0.73 precursor (AACT_HUMAN) TMMVLVNYIFF K.A alpha-1-antichymotrypsin P01011 R.DYNLNDILLQ 0.74 0.62 precursor (AACT_HUMAN) LGIEEAFTSK.A alpha-1-antichymotrypsin P01011 R.GTHVDLGLAS 0.76 0.61 precursor (AACT_HUMAN) ANVDFAFSLYK.Q alpha-1B-glycoprotein P04217 K.SLPAPWLSMA 0.71 0.65 precursor (A1BG_HUMAN) PVSWITPGLK.T alpha-2-antiplasmin P08697 K.GFPIKEDFLEQ 0.66 0.69 isoform a precursor (A2AP_HUMAN) SEQLFGAKPVSL TGK.Q alpha-2-antiplasmin P08697 K.HQMDLVATL 0.67 0.60 isoform a precursor (A2AP_HUMAN) SQLGLQELFQAP DLR.G alpha-2-antiplasmin P08697 R.QLTSGPNQEQ 0.66 0.61 isoform a precursor (A2AP_HUMAN) VSPLTLLK.L alpha-2-HS-glycoprotein P02765 R.AQLVPLPPST 0.64 0.63 preproprotein (FETUA_HUMAN) YVEFTVSGTDC VAK.E angiotensinogen P01019 K.DPTFIPAPIQA 0.69 0.69 preproprotein (ANGT_HUMAN) K.T angiotensinogen P01019 R.FM*QAVTGW 0.65 0.65 preproprotein (ANGT_HUMAN) K.T antithrombin-III P01008 K.ANRPFLVFIR.E 0.72 0.60 precursor (ANT3_HUMAN) antithrombin-III P01008 K.GDDITMVLIL 0.69 0.68 precursor (ANT3_HUMAN) PKPEK.S antithrombin-III P01008 R.DIPMNPMCIY 0.63 0.78 precursor (ANT3_HUMAN) R.S apolipoprotein A-IV P06727 K.KLVPFATELH 0.65 0.77 precursor (APOA4_HUMAN) ER.L apolipoprotein A-IV P06727 K.SLAELGGHLD 0.60 0.75 precursor (APOA4_HUMAN) QQVEEFR.R apolipoprotein B-100 P04114 K.ALYWVNGQV 0.61 0.63 precursor (APOB_HUMAN) PDGVSK.V apolipoprotein B-100 P04114 K.FIIPGLK.L 0.64 0.68 precursor (APOB_HUMAN) apolipoprotein B-100 P04114 K.FSVPAGIVIPS 0.63 0.63 precursor (APOB_HUMAN) FQALTAR.F apolipoprotein B-100 P04114 K.IEGNLIFDPNN 0.63 0.65 precursor (APOB_HUMAN) YLPK.E apolipoprotein B-100 P04114 K.LNDLNSVLV 0.91 0.88 precursor (APOB_HUMAN) MPTFHVPFTDL QVPSCK.L apolipoprotein B-100 P04114 K.VELEVPQLCS 0.60 0.61 precursor (APOB_HUMAN) FILK.T apolipoprotein B-100 P04114 K.VNWEEEAAS 0.60 0.73 precursor (APOB_HUMAN) GLLTSLK.D apolipoprotein B-100 P04114 R.ATLYALSHAV 0.78 0.80 precursor (APOB_HUMAN) NNYHK.T apolipoprotein B-100 P04114 R.TGISPLALIK.G 0.64 0.77 precursor (APOB_HUMAN) apolipoprotein B-100 P04114 R.TLQGIPQMIG 0.65 0.66 precursor (APOB_HUMAN) EVIR.K apolipoprotein C-III P02656 K.DALSSVQESQ 0.80 0.69 precursor (APOC3_HUMAN) VAQQAR.G apolipoprotein C-IV P55056 R.DGWQWFWSP 0.63 0.67 precursor (APOC4_HUMAN) STFR.G apolipoprotein E P02649 K.VQAAVGTSA 0.70 0.72 precursor (APOE_HUMAN) APVPSDNH.- apolipoprotein E P02649 R.WELALGR.F 0.88 0.60 precursor (APOE_HUMAN) beta-2-microglobulin P61769 K.SNFLNCYVSG 0.60 0.70 precursor (B2MG_HUMAN) FHPSDIEVDLLK.N bone marrow P13727 R.GGHCVALCT 0.83 0.86 proteoglycan isoform 1 (PRG2_HUMAN) R.G preproprotein carboxypeptidase B2 Q96IY4 R.LVDFYVMPV 0.61 0.65 preproprotein (CBPB2_HUMAN) VNVDGYDYSW K.K carboxypeptidase B2 Q96IY4 R.YTHGHGSETL 0.60 0.68 preproprotein (CBPB2_HUMAN) YLAPGGGDDWI YDLGIK.Y carboxypeptidase N P22792 K.LSNNALSGLP 0.65 0.67 subunit 2 precursor (CPN2_HUMAN) QGVFGK.L carboxypeptidase N P22792 K.TLNLAQNLLA 0.67 0.69 subunit 2 precursor (CPN2_HUMAN) QLPEELFHPLTS LQTLK.L carboxypeptidase N P22792 R.WLNVQLSPR.Q 0.74 0.67 subunit 2 precursor (CPN2_HUMAN) ceruloplasmin precursor P00450 K.GDSVVWYLF 0.90 0.72 (CERU_HUMAN) SAGNEADVHGI YFSGNTYLWR.G ceruloplasmin precursor P00450 K.MYYSAVDPT 0.70 0.82 (CERU_HUMAN) K.D ceruloplasmin precursor P00450 R.GPEEEHLGIL 0.60 0.65 (CERU_HUMAN) GPVIWAEVGDTI R.V ceruloplasmin precursor P00450 R.IDTINLFPATL 0.66 0.70 (CERU_HUMAN) FDAYMVAQNP GEWMLSCQNL NHLK.A ceruloplasmin precursor P00450 R.SGAGTEDSAC 0.88 0.92 (CERU_HUMAN) IPWAYYSTVDQ VKDLYSGLIGPL IVCR.R cholinesterase precursor P06276 K.IFFPGVSEFGK 0.70 0.63 (CHLE_HUMAN) .E cholinesterase precursor P06276 R.AILQSGSFNAP 0.75 0.77 (CHLE_HUMAN) WAVTSLYEAR.N chorionic gonadotropin, P01233 R.VLQGVLPALP 0.60 0.75 beta polypeptide 8 (CGHB_HUMAN) QVVCNYR.D precursor chorionic P01243 R.ISLLLIESWLE 0.83 0.63 somatomammotropin (CSH_HUMAN) PVR.F hormone 2 isoform 2 precursor coagulation factor XII P00748 R.LHEAFSPVSY 0.60 0.66 precursor (FA12_HUMAN) QHDLALLR.L coagulation factor XII P00748 R.TTLSGAPCQP 0.69 0.82 precursor (FA12_HUMAN) WASEATYR.N complement C1q P02745 K.GLFQVVSGG 0.65 0.60 subcomponent subunit A (C1QA_HUMAN) MVLQLQQGDQ precursor VWVEKDPK.K complement C1r P00736 K.VLNYVDWIK 0.80 0.76 subcomponent precursor (C1R_HUMAN) K.E complement C1s P09871 K.SNALDIIFQTD 0.62 0.77 subcomponent precursor (C1S_HUMAN) LTGQK.K complement C4-A P0C0L4 K.EGAIHREELV 0.76 0.75 isoform 1 (CO4A_HUMAN) YELNPLDHR.G complement C4-A P0C0L4 K.ITQVLHFTK.D 0.63 0.62 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 K.SHALQLNNR.Q 0.66 0.71 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 R.AVGSGATFSH 0.65 0.60 isoform 1 (CO4A_HUMAN) YYYM*ILSR.G complement C4-A P0C0L4 R.EPFLSCCQFA 0.64 0.72 isoform 1 (CO4A_HUMAN) ESLR.K complement C4-A P0C0L4 R.GHLFLQTDQP 0.63 0.76 isoform 1 (CO4A_HUMAN) IYNPGQR.V complement C4-A P0C0L4 R.GLEEELQFSL 0.68 0.68 isoform 1 (CO4A_HUMAN) GSK.I complement C4-A P0C0L4 R.GSFEFPVGDA 0.67 0.70 isoform 1 (CO4A_HUMAN) VSK.V complement C4-A P0C0L4 R.LLATLCSAEV 0.61 0.71 isoform 1 (CO4A_HUMAN) CQCAEGK.C complement C4-A P0C0L4 R.VQQPDCREPF 0.65 0.83 isoform 1 (CO4A_HUMAN) LSCCQFAESLRK .K complement C4-A P0C0L4 R.YIYGKPVQGV 0.82 0.76 isoform 1 (CO4A_HUMAN) AYVR.F complement C5 P01031 K.ITHYNYLILSK 0.66 0.69 preproprotein (CO5_HUMAN) .G complement C5 P01031 R.ENSLYLTAFT 0.60 0.68 preproprotein (CO5_HUMAN) VIGIR.K complement C5 P01031 R.KAFDICPLVK.I 0.77 0.65 preproprotein (CO5_HUMAN) complement C5 P01031 R.VDDGVASFVL 0.68 0.61 preproprotein (CO5_HUMAN) NLPSGVTVLEFN VK.T complement component P13671 K.TFSEWLESVK 0.94 0.64 C6 precursor (CO6_HUMAN) ENPAVIDFELAP IVDLVR.N complement component P13671 R.IFDDFGTHYF 0.78 0.75 C6 precursor (CO6_HUMAN) TSGSLGGVYDL LYQFSSEELK.N complement component P10643 K.ELSHLPSLYD 0.69 0.71 C7 precursor (CO7_HUMAN) YSAYR.R complement component P10643 R.RYSAWAESV 0.71 0.70 C7 precursor (CO7_HUMAN) TNLPQVIK.Q complement component P07357 K.YNPVVIDFEM 0.68 0.73 C8 alpha chain precursor (CO8A_HUMAN) *QPIHEVLR.H complement component P07358 K.VEPLYELVTA 0.69 0.70 C8 beta chain (CO8B_HUMAN) TDFAYSSTVR.Q preproprotein complement component P07358 R.SLM*LHYEFL 0.61 0.65 C8 beta chain (CO8B_HUMAN) QR.V preproprotein complement component P07360 K.YGFCEAADQF 0.78 0.76 C8 gamma chain (CO8G_HUMAN) HVLDEVRR.- precursor complement component P07360 R.FLQEQGHR.A 0.63 0.69 C8 gamma chain (CO8G_HUMAN) precursor complement component P07360 R.KLDGICWQV 0.75 0.70 C8 gamma chain (CO8G_HUMAN) R.Q precursor complement component P07360 R.SLPVSDSVLS 0.70 0.60 C8 gamma chain (CO8G_HUMAN) GFEQR.V precursor complement component P02748 R.GTVIDVTDFV 0.68 0.69 C9 precursor (CO9_HUMAN) NWASSINDAPV LISQK.L complement factor B P00751 K.NPREDYLDV 0.72 0.77 preproprotein (CFAB_HUMAN) YVFGVGPLVNQ VNINALASK.K complement factor B P00751 R.GDSGGPLIVH 0.60 0.76 preproprotein (CFAB_HUMAN) KR.S complement factor B P00751 R.HVIILMTDGL 0.60 0.64 preproprotein (CFAB_HUMAN) HNM*GGDPITVI DEIR.D complement factor B P00751 R.KNPREDYLDV 0.63 0.63 preproprotein (CFAB_HUMAN) YVFGVGPLVNQ VNINALASK.K complement factor H P08603 K.SCDIPVFMNA 0.62 0.71 isoform a precursor (CFAH_HUMAN) R.T complement factor H P08603 K.SPPEISHGVV 0.88 0.88 isoform a precursor (CFAH_HUMAN) AHMSDSYQYGE EVTYK.C complement factor H P08603 K.TDCLSLPSFE 0.61 0.66 isoform a precursor (CFAH_HUMAN) NAIPMGEKK.D complement factor I P05156 K.RAQLGDLPW 0.71 0.74 preproprotein (CFAI_HUMAN) QVAIK.D complement factor I P05156 K.SLECLHPGTK.F 0.64 0.81 preproprotein (CFAI_HUMAN) complement factor I P05156 R.TMGYQDFAD 0.73 0.75 preproprotein (CFAI_HUMAN) VVCYTQK.A extracellular matrix Q16610 R.ELLALIQLER.E 0.69 0.65 protein 1 isoform 3 (ECM1_HUMAN) precursor gelsolin isoform a P06396 R.VPEARPNSMV 0.76 0.62 precursor (GELS_HUMAN) VEHPEFLK.A glutathione peroxidase 3 P22352 R.LFWEPMK.V 0.69 0.67 precursor (GPX3_HUMAN) hemopexin precursor P02790 R.DVRDYFMPCP 0.70 0.72 (HEMO_HUMAN) GR.G heparin cofactor 2 P05546 K.DALENIDPAT 0.61 0.65 precursor (HEP2_HUMAN) QMMILNCIYFK.G heparin cofactor 2 P05546 K.GLIKDALENI 0.64 0.64 precursor (HEP2_HUMAN) DPATQMMILNC IYFK.G heparin cofactor 2 P05546 K.QFPILLDFK.T 0.61 0.69 precursor (HEP2_HUMAN) heparin cofactor 2 P05546 R.VLKDQVNTF 0.88 0.75 precursor (HEP2_HUMAN) DNIFIAPVGISTA MGMISLGLK.G insulin-like growth P35858 R.AFWLDVSHN 0.61 0.82 factor-binding protein (ALS_HUMAN) R.L complex acid labile subunit isoform 2 precursor inter-alpha-trypsin P19827 K.ADVQAHGEG 0.61 0.74 inhibitor heavy chain H1 (ITIH1_HUMAN) QEFSITCLVDEE isoform a precursor EMKK.L inter-alpha-trypsin P19827 K.ILGDM*QPGD 0.71 0.63 inhibitor heavy chain H1 (ITIH1_HUMAN) YFDLVLFGTR.V isoform a precursor inter-alpha-trypsin P19827 K.ILGDMQPGDY 0.68 0.60 inhibitor heavy chain H1 (ITIH1_HUMAN) FDLVLFGTR.V isoform a precursor inter-alpha-trypsin P19827 K.NVVFVIDISGS 0.76 0.83 inhibitor heavy chain H1 (ITIH1_HUMAN) MR.G isoform a precursor inter-alpha-trypsin P19827 K.TAFISDFAVT 0.74 0.63 inhibitor heavy chain H1 (ITIH1_HUMAN) ADGNAFIGDIKD isoform a precursor K.V inter-alpha-trypsin P19827 R.GHMLENHVE 0.78 0.80 inhibitor heavy chain H1 (ITIH1_HUMAN) R.L isoform a precursor inter-alpha-trypsin P19827 R.GM*ADQDGL 0.61 0.62 inhibitor heavy chain H1 (ITIH1_HUMAN) KPTIDKPSEDSP isoform a precursor PLEMLGPR.R inter-alpha-trypsin P19827 R.LWAYLTIQEL 0.68 0.62 inhibitor heavy chain H1 (ITIH1_HUMAN) LAK.R isoform a precursor inter-alpha-trypsin P19827 R.NHM*QYEIVI 0.67 0.65 inhibitor heavy chain H1 (ITIH1_HUMAN) K.V isoform a precursor inter-alpha-trypsin P19823 K.AHVSFKPTVA 0.75 0.61 inhibitor heavy chain H2 (ITIH2_HUMAN) QQR.I precursor inter-alpha-trypsin P19823 K.ENIQDNISLFS 0.80 0.93 inhibitor heavy chain H2 (ITIH2_HUMAN) LGM*GFDVDYD precursor FLKR.L inter-alpha-trypsin P19823 K.ENIQDNISLFS 0.63 0.80 inhibitor heavy chain H2 (ITIH2_HUMAN) LGMGFDVDYDF precursor LKR.L inter-alpha-trypsin P19823 K.HLEVDVWVIE 0.61 0.61 inhibitor heavy chain H2 (ITIH2_HUMAN) PQGLR.F precursor inter-alpha-trypsin P19823 K.LWAYLTINQL 0.69 0.62 inhibitor heavy chain H2 (ITIH2_HUMAN) LAER.S precursor inter-alpha-trypsin P19823 R.AEDHFSVIDF 0.65 0.63 inhibitor heavy chain H2 (ITIH2_HUMAN) NQNIR.T precursor inter-alpha-trypsin P19823 R.FLHVPDTFEG 0.66 0.62 inhibitor heavy chain H2 (ITIH2_HUMAN) HFDGVPVISK.G precursor inter-alpha-trypsin Q14624 K.ILDDLSPR.D 0.67 0.65 inhibitor heavy chain H4 (ITIH4_HUMAN) isoform 1 precursor inter-alpha-trypsin Q14624 K.IPKPEASFSPR.R 0.69 0.77 inhibitor heavy chain H4 (ITIH4_HUMAN) isoform 1 precursor inter-alpha-trypsin Q14624 K.SPEQQETVLD 0.63 0.69 inhibitor heavy chain H4 (ITIH4_HUMAN) GNLIIR.Y isoform 1 precursor inter-alpha-trypsin Q14624 K.YIFHNFMER.L 0.66 0.61 inhibitor heavy chain H4 (ITIH4_HUMAN) isoform 1 precursor inter-alpha-trypsin Q14624 R.FSSHVGGTLG 0.69 0.71 inhibitor heavy chain H4 (ITIH4_HUMAN) QFYQEVLWGSP isoform 1 precursor AASDDGRR.T inter-alpha-trypsin Q14624 R.GPDVLTATVS 0.63 0.82 inhibitor heavy chain H4 (ITIH4_HUMAN) GK.L isoform 1 precursor inter-alpha-trypsin Q14624 R.NMEQFQVSVS 0.78 0.60 inhibitor heavy chain H4 (ITIH4_HUMAN) VAPNAK.I isoform 1 precursor inter-alpha-trypsin Q14624 R.RLDYQEGPPG 0.68 0.62 inhibitor heavy chain H4 (ITIH4_HUMAN) VEISCWSVEL.- isoform 1 precursor kallistatin precursor P29622 K.IVDLVSELKK.D 0.75 0.67 (KAIN_HUMAN) kallistatin precursor P29622 R.VGSALFLSHN 0.70 0.74 (KAIN_HUMAN) LK.F kininogen-1 isoform 2 P01042 K.IYPTVNCQPL 0.89 0.62 precursor (KNG1_HUMAN) GM*ISLM*K.R kininogen-1 isoform 2 P01042 K.TVGSDTFYSF 0.61 0.68 precursor (KNG1_HUMAN) K.Y kininogen-1 isoform 2 P01042 R.DIPTNSPELEE 0.61 0.76 precursor (KNG1_HUMAN) TLTHTITK.L kininogen-1 isoform 2 P01042 R.VQVVAGK.K 0.67 0.71 precursor (KNG1_HUMAN) lumican precursor P51884 R.FNALQYLR.L 0.68 0.76 (LUM_HUMAN) macrophage colony- P09603 K.VIPGPPALTLV 0.68 0.60 stimulating factor 1 (CSF1_HUMAN) PAELVR.I receptor precursor monocyte differentiation P08571 K.ITGTMPPLPLE 0.80 0.67 antigen CD14 precursor (CD14_HUMAN) ATGLALSSLR.L N-acetylmuramoyl-L- Q96PD5 K.EFTEAFLGCP 0.62 0.64 alanine amidase (PGRP2_HUMAN) AIHPR.C precursor N-acetylmuramoyl-L- Q96PD5 R.RVINLPLDSM 0.63 0.62 alanine amidase (PGRP2_HUMAN) AAPWETGDTFP precursor DVVAIAPDVR.A phosphatidylinositol- P80108 R.GVFFSVNSWT 0.67 0.78 glycan-specific (PHLD_HUMAN) PDSMSFIYK.A phospholipase D precursor pigment epithelium- P36955 K.EIPDEISILLLGVAHF 0.63 0.61 derived factor precursor (PEDF_HUMAN) K.G pigment epithelium- P36955 K.IAQLPLTGSM*SIIF 0.79 0.61 derived factor precursor (PEDF_HUMAN) FLPLK.V pigment epithelium- P36955 K.TVQAVLTVPK.L 0.75 0.79 derived factor precursor (PEDF_HUMAN) pigment epithelium- P36955 R.ALYYDLISSPDIHGT 0.60 0.73 derived factor precursor (PEDF_HUMAN) YKELLDTVTAPQK.N pigment epithelium- P36955 R.DTDTGALLFIGK.I 0.85 0.62 derived factor precursor (PEDF_HUMAN) plasminogen isoform 1 P00747 R.ELRPWCFTTDPNK 0.70 0.68 precursor (PLMN_HUMAN) R.W plasminogen isoform 1 P00747 R.TECFITGWGETQGT 0.63 0.68 precursor (PLMN_HUMAN) FGAGLLK.E platelet basic protein P02775 K.GTHCNQVEVIATLK 0.60 0.61 preproprotein (CXCL7_HUMAN) .D pregnancy zone protein P20742 K.AVGYLITGYQR.Q 0.87 0.73 precursor (PZP_HUMAN) pregnancy zone protein P20742 R.AVDQSVLLM*KPE 0.64 0.62 precursor (PZP_HUMAN) AELSVSSVYNLLTVK.D pregnancy zone protein P20742 R.IQHPFTVEEFVLPK.F 0.66 0.74 precursor (PZP_HUMAN) pregnancy zone protein P20742 R.NELIPLIYLENPR.R 0.61 0.61 precursor (PZP_HUMAN) protein AMBP P02760 R.AFIQLWAFDAVK.G 0.72 0.67 preproprotein (AMBP_HUMAN) proteoglycan 4 isoform B Q92954 K.GFGGLTGQIVAALS 0.70 0.72 precursor (PRG4_HUMAN) TAK.Y prothrombin preproprotein P00734 K.YGFYTHVFR.L 0.70 0.63 (THRB_HUMAN) prothrombin preproprotein P00734 R.IVEGSDAEIGM*SP 0.63 0.71 (THRB_HUMAN) WQVMLFR.K retinol-binding protein 4 P02753 K.KDPEGLFLQDNIVA 0.67 0.67 precursor (RET4_HUMAN) EFSVDETGQMSATAK .G thyroxine-binding globulin P05543 K.AQWANPFDPSKTE 0.67 0.80 precursor (THBG_HUMAN) DSSSFLIDK.T thyroxine-binding globulin P05543 K.GWVDLFVPK.F 0.67 0.64 precursor (THBG_HUMAN) thyroxine-binding globulin P05543 R.SFM*LLILER.S 0.65 0.68 precursor (THBG_HUMAN) thyroxine-binding globulin P05543 R.SFMLLILER.S 0.64 0.62 precursor (THBG_HUMAN) vitamin D-binding protein P02774 K.EFSHLGKEDFTSLSL 0.74 0.61 isoform 1 precursor (VTDB_HUMAN) VLYSR.K vitamin D-binding protein P02774 K.EYANQFM*WEYST 0.73 0.61 isoform 1 precursor (VTDB_HUMAN) NYGQAPLSLLVSYTK.S vitamin D-binding protein P02774 K.HQPQEFPTYVEPTN 0.67 0.69 isoform 1 precursor (VTDB_HUMAN) DEICEAFRK.D vitamin D-binding protein P02774 K.SYLSM*VGSCCTSA 0.63 0.62 isoform 1 precursor (VTDB_HUMAN) SPTVCFLK.E vitamin D-binding protein P02774 K.TAM*DVFVCTYFM 0.63 0.60 isoform 1 precursor (VTDB_HUMAN) PAAQLPELPDVELPT NK.D vitamin D-binding protein P02774 K.VPTADLEDVLPLAE 0.70 0.71 isoform 1 precursor (VTDB_HUMAN) DITNILSK.C vitronectin precursor P04004 K.AVRPGYPK.L 0.68 0.77 (VTNC_HUMAN) vitronectin precursor P04004 R.MDWLVPATCEPIQ 0.67 0.65 (VTNC_HUMAN) SVFFFSGDK.Y zinc-alpha-2-glycoprotein P25311 K.EIPAWVPFDPAAQI 0.63 0.67 precursor (ZA2G_HUMAN) TK.Q

The differentially expressed proteins identified by the hypothesis-independent strategy above, not already present in our MRM-MS assay, were candidates for incorporation into the MRM-MS assay. Two additional proteins (AFP, PGH1) of functional interest were also selected for MRM development. Candidates were prioritized by AUC and biological function, with preference give for new pathways. Sequences for each protein of interest, were imported into Skyline software which generated a list of tryptic peptides, m/z values for the parent ions and fragment ions, and an instrument-specific collision energy (McLean et al. Bioinformatics (2010) 26 (7): 966-968; McLean et al. Anal. Chem (2010) 82 (24): 10116-10124).

The list was refined by eliminating peptides containing cysteines and methionies, and by using the shotgun data to select the charge state(s) and a subset of potential fragment ions for each peptide that had already been observed on a mass spectrometer.

After prioritizing parent and fragment ions, a list of transitions was exported with a single predicted collision energy. Approximately 100 transitions were added to a single MRM run. For development, MRM data was collected on either a QTRAP 5500 (AB Sciex) or a 6490 QQQ (Agilent). Commercially available human female serum (from pregnant and non-pregnant donors), was depleted and processed to tryptic peptides, as described above, and used to “scan” for peptides of interest. In some cases, purified synthetic peptides were used for further optimization. For development, digested serum or purified synthetic peptides were separated with a 15 min acetonitrile gradient at 100 ul/min on a 2.1×50 mM Poroshell 120 EC-C18 column (Agilent) at 40° C.

The MS/MS data was imported back into Skyline, where all chromatograms for each peptide were overlayed and used to identify a concensus peak corresponding to the peptide of interest and the transitions with the highest intensities and the least noise. Table 11, contains a list of the most intensely observed candidate transitions and peptides for transfer to the MRM assay.

TABLE 11 Candidate peptides and transitions for transferring to the MRM assay fragment ion, m/z, Protein Peptide m/z, charge charge, rank area alpha-1-antichymotrypsin K.ADLSGITGAR.N 480.7591++ S [y7] - 661.3628+[1] 1437602   G [y6] - 574.3307+[2] 637584  T [y4] - 404.2252+[3] 350392  L [y8] - 774.4468+[4] 191870  G [y3] - 303.1775+[5] 150575  I [y5] - 517.3093+[6] 97828 alpha-1-antichymotrypsin K.EQLSLLDR.F 487.2693++ S [y5] - 603.3461+[1] 345602  L [y6] - 716.4301+[2] 230046  L [y4] - 516.3140+[3] 143874  D [y2] - 290.1459+[4] 113381  D [y2] - 290.1459+[5] 113381  Q [b2] - 258.1084+[6] 78157 alpha-1-antichymotrypsin K.ITLLSALVETR.T 608.3690++ S [y7] - 775.4308+[1] 1059034   L [y8] - 888.5149+[2] 541969  T [b2] - 215.1390+[3] 408819  L [y9] - 1001.5990+[4] 438441  V [y4] - 504.2776+[5] 311293  L [y5] - 617.3617+[6] 262544  L [b3] - 328.2231+[7] 197526  T [y2] - 276.1666+[8] 212816  E [y3] - 405.2092+[9] 207163  alpha-1-antichymotrypsin R.EIGELYLPK.F 531.2975++ G [y7] - 819.4611+[2] 977307  L [y5] - 633.3970+[3] 820582  Y [y4] - 520.3130+[4] 400762  L [y3] - 357.2496+[5] 498958  P [y2] - 244.1656+[1] 1320591   I [b2] - 243.1339+[6] 303268  G [b3] - 300.1554+[7] 305120  alpha-1-antichymotrypsin R.GTHVDLGLASA 742.3794+++ D [y8] - 990.4931+[1] 154927  NVDFAFSLYK.Q L [b8] - 793.4203+[2] 51068 D [b5] - 510.2307+[3] 45310 F [y7] - 875.4662+[4] 42630 A [b9] - 864.4574+[5] 43355 S [y4] - 510.2922+[6] 45310 F [y5] - 657.3606+[7] 37330 V [y9] - 1089.5615+[8] 32491 G [b7] - 680.3362+[9] 38185 Y [y2] - 310.1761+[10] 36336 N [b12] - 16389 1136.5695+[11] S [b10] - 951.4894+[12] 16365 L [b6] - 623.3148+[13] 13687 L [y3] - 423.2602+[14] 17156 V [b4] - 395.2037+[15] 10964 alpha-1-antichymotrypsin R.NLAVSQVVHK.A 547.8195++ A [y8] - 867.5047+[1] 266203  L [b2] - 228.1343+[2] 314232  V [y7] - 796.4676+[3] 165231  A [b3] - 299.1714+[4] 173694  S [y6] - 697.3991+[5] 158512  H [y2] - 284.1717+[6] 136431  V [b4] - 398.2398+[7] 36099 S [b5] - 485.2718+[8] 23836 365.5487+++ S [y6] - 697.3991+[1] 223443  V [y3] - 383.2401+[2] 112952  V [y4] - 482.3085+[3] 84872 Q [y5] - 610.3671+[4] 30835 inter-alpha-trypsin K.AAISGENAGLVR 579.3173++ S [y9] - 902.4690+[1] 518001  inhibitor heavy chain H1 .A G [y8] - 815.4370+[2] 326256  N [y6] - 629.3729+[3] 296670  S [b4] - 343.1976+[4] 258172  inter-alpha-trypsin K.GSLVQASEANL 668.6763+++ A [y7] - 806.4155+[1] 304374  inhibitor heavy chain H1 QAAQDFVR.G A [y6] - 735.3784+[2] 193844  V [b4] - 357.2132+[3] 294094  F [y3] - 421.2558+[4] 167816  A [b6] - 556.3089+[5] 149216  L [b11] - 535.7775++[6] 156882  A [b13] - 635.3253++[7] 249287  A [y14] - 760.3786++[8] 123723  F [b17] - 865.9208++[9] 23057 inter-alpha-trypsin K.TAFISDFAVTAD 1087.0442++ G [y4] - 432.2453+[1] 22362 inhibitor heavy chain H1 GNAFIGDIK.D I [y5] - 545.3293+[2]  8319 A [b8] - 853.4090+[3]  7006 G [y9] - 934.4993+[4]  6755 F [y6] - 692.3978+[5]  6193 V [b9] - 952.4775+[6]  9508 inter-alpha-trypsin K.VTYDVSR.D 420.2165++ Y [y5] - 639.3097+[1] 609348  inhibitor heavy chain H1 T [b2] - 201.1234+[2] 792556  D [y4] - 476.2463+[3] 169546  V [y3] - 361.2194+[4] 256946  Y [y5] - 320.1585++[5] 110608  S [y2] - 262.1510+[6] 50268 Y [b3] - 182.5970++[7] 10947 D [b4] - 479.2136+[8] 13662 inter-alpha-trypsin R.EVAFDLEIPK.T 580.8135++ P [y2] - 244.1656+[1] 2032509   inhibitor heavy chain H1 D [y6] - 714.4032+[2] 672749  A [y8] - 932.5088+[3] 390837  L [y5] - 599.3763+[4] 255527  F [y7] - 861.4716+[5] 305087  inter-alpha-trypsin R.LWAYLTIQELLA 781.4531++ W [b2] - 300.1707+[1] 602601  inhibitor heavy chain H1 K.R A [b3] - 371.2078+[2] 356967  T [y8] - 915.5510+[3] 150419  Y [b4] - 534.2711+[4] 103449  I [y7] - 814.5033+[5] 72044 Q [y6] - 701.4192+[6] 66989 L [b5] - 647.3552+[7] 99820 E [y5] - 573.3606+[8] 44843 inter-alpha-trypsin K.FYNQVSTPLLR.N 669.3642++ S [y6] - 686.4196+[1] 367330  inhibitor heavy chain H2 V [y7] - 785.4880+[2] 182396  P [y4] - 498.3398+[3] 103638  Y [b2] - 311.1390+[4] 52172 Q [b4] - 553.2405+[5] 54270 N [b3] - 425.1819+[6] 34567 inter-alpha-trypsin K.HLEVDVWVIEP 597.3247+++ I [y7] - 812.4625+[1] 206996  inhibitor heavy chain H2 QGLR.F P [y5] - 570.3358+[2] 303693  E [y6] - 699.3784+[3] 126752  P [y5] - 285.6715++[4] 79841 inter-alpha-trypsin K.TAGLVR.S 308.6925++ A [b2] - 173.0921+[1] 460019  inhibitor heavy chain H2 G [y4] - 444.2929+[2] 789068  V [y2] - 274.1874+[3] 34333 G [b3] - 230.1135+[4] 15169 L [y3] - 387.2714+[5] 29020 inter-alpha-trypsin R.IYLQPGR.L 423.7452++ L [y5] - 570.3358+[1] 638209  inhibitor heavy chain H2 P [y3] - 329.1932+[2] 235194  Y [b2] - 277.1547+[3] 266889  Q [y4] - 457.2518+[4] 171389  inter-alpha-trypsin R.LSNENHGIAQR.I 413.5461+++ N [y9] - 519.7574++[1] 325409  inhibitor heavy chain H2 N [y7] - 398.2146++[2] 39521 G [y5] - 544.3202+[3] 139598  S [b2] - 201.1234+[4] 54786 E [y8] - 462.7359++[5] 30623 inter-alpha-trypsin R.SLAPTAAAKR.R 415.2425++ A [y7] - 629.3617+[1] 582421  inhibitor heavy chain H2 L [b2] - 201.1234+[2] 430584  P [y6] - 558.3246+[3] 463815  A [b3] - 272.1605+[4] 204183  T [y5] - 461.2718+[5] 47301 inter-alpha-trypsin K.EVSFDVELPK.T 581.8032++ P [y2] - 244.1656+[1] 132304  inhibitor heavy chain H3 V [b2] - 229.1183+[2] 48895 L [y3] - 357.2496+[3] 20685 inter-alpha-trypsin K.IQENVR.N 379.7114++ E [y4] - 517.2729+[1] 190296  inhibitor heavy chain H3 E [b3] - 371.1925+[2] 51697 Q [b2] - 242.1499+[3] 54241 N [y3] - 388.2303+[4] 21156 V [y2] - 274.1874+[5]  8309 inter-alpha-trypsin R.ALDLSLK.Y 380.2342++ D [y5] - 575.3399+[1] 687902  inhibitor heavy chain H3 L [b2] - 185.1285+[2] 241010  L [y2] - 260.1969+[3] 29365 inter-alpha-trypsin R.LIQDAVTGLTVN 972.0258++ V [b6] - 640.3665+[1] 139259  inhibitor heavy chain H3 GQITGDK.R G [b8] - 798.4356+[2] 53886 G [y7] - 718.3730+[3] 12518 pigment epithelium- K.SSFVAPLEK.S 489.2687++ A [y5] - 557.3293+[1] 13436 derived factor precursor V [y6] - 656.3978+[2]  9350 F [y7] - 803.4662+[3]  6672 P [y4] - 486.2922+[4]  6753 pigment epithelium- K.TVQAVLTVPK.L 528.3266++ Q [y8] - 855.5298+[1] 26719 derived factor precursor V [b2] - 201.1234+[2] 21239 Q [y8] - 428.2686++[3] 16900 A [y7] - 727.4713+[4]  9518 L [y5] - 557.3657+[5]  5108 Q [b3] - 329.1819+[6]  5450 V [y6] - 656.4341+[7]  4391 pigment epithelium- R.ALYYDLISSPDIH 652.6632+++ Y [y15] - 886.4305++[1] 78073 derived factor precursor GTYK.E Y [y14] - 804.8988++[2] 26148 pigment epithelium- R.DTDTGALLFIGK.I 625.8350++ G [y8] - 818.5135+[1] 25553 derived factor precursor T [b2] - 217.0819+[2] 22716 T [b4] - 217.0819++[3] 22716 L [y5] - 577.3708+[4] 11600 I [y3] - 317.2183+[5] 11089 A [b6] - 561.2151+[6]  6956 pigment epithelium- K.ELLDTVTAPQK.N 607.8350++ T [y5] - 544.3089+[1] 17139 derived factor precursor D [y8] - 859.4520+[2] 17440 L [y9] - 972.5360+[3] 14344 A [y4] - 443.2613+[4] 11474 T [y7] - 744.4250+[5] 10808 V [y6] - 643.3774+[6]  9064 pregnancy-specific beta- K.FQLPGQK.L 409.2320++ L [y5] - 542.3297+[1] 116611  1-glycoprotein 1 P [y4] - 429.2456+[2] 91769 Q [b2] - 276.1343+[3] 93301 pregnancy-specific beta- R.DLYHYITSYVVD 955.4762+++ G [y7] - 707.3471+[1]  5376 1-glycoprotein 1 GEIIIYGPAYSGR.E Y [y8] - 870.4104+[2]  3610 P [y6] - 650.3257+[3]  2770 I [y9] - 983.4945+[4]  3361 pregnancy-specific beta- K.LFIPQITPK.H 528.8262++ P [y6] - 683.4087+[1] 39754 1-glycoprotein 11 F [b2] - 261.1598+[2] 29966 I [y7] - 796.4927+[3] 13162 pregnancy-specific beta- NSATGEESSTSLTIR 776.8761++ E [b7] - 689.2737+[1] 11009 1-glycoprotein 11 T [y6] - 690.4145+[2] 11284 L [y4] - 502.3348+[3]  2265 S [y7] - 389.2269++[4]  1200 T [y3] - 389.2507+[5]  1200 I [y2] - 288.2030+[6]  2248 pregnancy-specific beta- K.FQQSGQNLFIP 617.3317+++ F [y8] - 474.2817++[1] 43682 1-glycoprotein 2 QITTK.H G [y12] - 680.3852++[2] 24166 S [b4] - 491.2249+[3] 23548 Q [b3] - 404.1928+[4] 17499 I [y4] - 462.2922+[5] 17304 F [b9] - 525.7538++[6] 17206 I [b10] - 582.2958++[7] 16718 L [b8] - 452.2196++[8] 16490 P [y6] - 344.2054++[9] 16198 G [b5] - 548.2463+[10] 15320 pregnancy-specific beta- IHPSYTNYR 575.7856++ N [b7] - 813.3890+[1] 16879 1-glycoprotein 2 Y [b5] - 598.2984+[2] 18087 T [y4] - 553.2729+[3]  2682 pregnancy-specific beta- FQLSETNR 497.7513++ L [y6] - 719.3682+[1] 358059  1-glycoprotein 2 S [y5] - 606.2842+[2] 182330  Q [b2] - 276.1343+[3] 292482  pregnancy-specific beta- VSAPSGTGHLPGL 506.2755+++ T [b7] - 300.6530++[1] 25346 1-glycoprotein 3 NPL H [y8] - 860.4989+[2] 12159 H [y8] - 430.7531++[3] 15522 pregnancy-specific beta- EDAGSYTLHIVK 666.8433++ Y [b6] - 623.2307+[1] 23965 1-glycoprotein 3 Y [y7] - 873.5193+[2] 21686 L [b8] - 837.3625+[3]  4104 A [b3] - 316.1139+[4]  1987 pregnancy-specific beta- R.TLFIFGVTK.Y 513.3051++ F [y7] - 811.4713+[1] 62145 1-glycoprotein 4 L [b2] - 215.1390+[2] 31687 F [y5] - 551.3188+[3]   972 pregnancy-specific beta- NYTYIWWLNGQS 1097.5576++ W [b6] - 841.3879+[1] 25756 1-glycoprotein 4 LPVSPR G [y9] - 940.5211+[2] 25018 Y [b4] - 542.2245+[3] 19778 Q [y8] - 883.4996+[4]  6642 P [y2] - 272.1717+[5]  5018 pregnancy-specific beta- GVTGYFTFNLYLK 508.2695+++ L [y2] - 260.1969+[1] 176797  1-glycoprotein 5 T [y11] - 683.8557++[2] 136231  F [b6] - 625.2980+[3] 47523 L [y4] - 536.3443+[4] 23513 pregnancy-specific beta- SNPVTLNVLYGPD 585.6527+++ Y [y7] - 817.4203+[1] 14118 1-glycoprotein 6 LPR G [y6] - 654.3570+[2] 10433 P [b3] - 299.1350+[3]  87138* P [y5] - 299.1714++[4]  77478* P [y5] - 597.3355+[5]  68089* pregnancy-specific beta- DVLLLVHNLPQNL 791.7741+++ L [y8] - 1017.5516+[3] 141169  1-glycoprotein 7 TGHIWYK G [y6] - 803.4199+[5] 115905  W [y3] - 496.2554+[6] 108565  P [y11] - 678.8566++[7] 105493  V [b2] - 215.1026+[1] 239492  L [b3] - 328.1867+[2] 204413  N [b8] - 904.5251+[4] 121880  pregnancy-specific beta- YGPAYSGR 435.7089++ A [y5] - 553.2729+[1]  25743* 1-glycoprotein 7 Y [y4] - 482.2358+[2]  25580* P [y6] - 650.3257+[3]  10831* S [y3] - 319.1724+[4]  10559* G [b2] - 221.0921+[5]   7837* pregnancy-specific beta- LQLSETNR 480.7591++ S [b4] - 442.2660+[1] 18766 1-glycoprotein 8 L [b3] - 355.2340+[2] 12050 Q [b2] - 242.1499+[3]  1339 T [b6] - 672.3563+[4]  2489 pregnancy-specific beta- K.LFIPQITR.N 494.3029++ P [y5] - 614.3620+[1] 53829 1-glycoprotein 9 I [y6] - 727.4461+[2] 13731 I [b3] - 374.2438+[3]  4178 Q [y4] - 517.3093+[4]  2984 pregnancy-specific beta- K.LPIPYITINNLNP 819.4723++ P [b2] - 211.1441+[1]  18814* 1-glycoprotein 9 R.E P [b4] - 211.1441++[2]  18814* T [b7] - 798.4760+[3]  17287* T [y8] - 941.5163+[4]  10205* Y [b5] - 584.3443+[5]  10136* N [y6] - 727.3846+[6]   9511* pregnancy-specific beta- R.SNPVILNVLYGP 589.6648+++ P [y5] - 597.3355+[1]  3994 1-glycoprotein 9 DLPR.I Y [y7] - 817.4203+[2]  3743 G [y6] - 654.3570+[3]  3045 pregnancy-specific beta- DVLLLVHNLPQNL 810.4387+++ P [y7] - 960.4614+[1] 120212  1-glycoprotein 9 PGYFWYK V [b2] - 215.1026+[2] 65494 L [b3] - 328.1867+[3] 54798 pregnancy-specific beta- SENYTYIWWLNG 846.7603+++ W [y15] - 834.4488++[1] 14788 1-glycoprotein 9 QSLPVSPGVK P [y4] - 200.6314++[2] 19000 Y [y17] - 972.5225++[3]  4596 L [b10] - 678.8166++[4]  2660 Y [b6] - 758.2992+[5]  1705 P [y4] - 400.2554+[6]  1847 Pan-PSG ILILPSVTR 506.3317++ P [y5] - 559.3198+[1] 484395  L [b2] - 227.1754+[2] 102774  L [b4] - 227.1754++[3] 102774  I [y7] - 785.4880+[4] 90153 I [b3] - 340.2595+[5] 45515 L [y6] - 672.4039+[6] 40368 thyroxine-binding K.AQWANPFDPS 630.8040++ A [b4] - 457.2194+[1] 30802 globulin precursor K.T S [y2] - 234.1448+[2] 28255 D [y4] - 446.2245+[3] 24933 thyroxine-binding K.AVLHIGEK.G 289.5080+++ I [y4] - 446.2609+[1] 220841  globulin precursor H [y5] - 292.1636++[2] 303815  H [y5] - 583.3198+[3] 133795  V [b2] - 171.1128+[4] 166139  L [y6] - 348.7056++[5] 823533  thyroxine-binding K.FLNDVK.T 368.2054++ N [y4] - 475.2511+[1] 296859  globulin precursor V [y2] - 246.1812+[2] 219597  L [b2] - 261.1598+[3] 87504 thyroxine-binding K.FSISATYDLGATL 800.4351++ Y [y9] - 993.5615+[1] 34111 globulin precursor LK.M G [y6] - 602.3872+[2] 17012 D [y8] - 830.4982+ 45104 S [b2] - 235.1077+[4] 15480 thyroxine-binding K.GWVDLFVPK.F 530.7949++ W [b2] - 244.1081+[1] 1261810   globulin precursor P [y2] - 244.1656+[2] 1261810   V [b7] - 817.4243+[3] 517675  V [y7] - 817.4818+[4] 517675  D [y6] - 718.4134+[5] 306994  F [b6] - 718.3559+[6] 306994  V [y3] - 343.2340+[7] 112565  V [b3] - 343.1765+[8] 112565  thyroxine-binding K.NALALFVLPK.E 543.3395++ A [y7] - 787.5076+[1] 198085  globulin precursor L [b3] - 299.1714+[2] 199857  P [y2] - 244.1656+[3] 129799  L [y8] - 900.5917+[4] 111572  L [y6] - 716.4705+[5] 88773 F [y5] - 603.3865+[6] 54020 L [y3] - 357.2496+[7] 43353 thyroxine-binding R.SILFLGK.V 389.2471++ L [y5] - 577.3708+[1] 1878736   globulin precursor I [b2] - 201.1234+[2] 946031  G [y2] - 204.1343+[3] 424248  L [y3] - 317.2183+[4] 291162  F [y4] - 464.2867+[5] 391171  AFP R.DFNQFSSGEK.N 386.8402+++ N [b3] - 189.0764++[1] 42543 S [y4] - 210.6081++[2] 21340 G [y3] - 333.1769+[3] 53766 N [b3] - 377.1456+[4] 58644 F [b2] - 263.1026+[5]  5301 AFP K.GYQELLEK.C 490.2584++ E [y5] - 631.3661+[1] 110518  L [y4] - 502.3235+[2] 74844 E [y2] - 276.1554+[3] 42924 E [b4] - 478.1932+[4] 20953 AFP K.GEEELQK.Y 416.7060++ E [b2] - 187.0713+[1] 37843 E [y4] - 517.2980+[2] 56988 AFP K.FIYEIAR.R 456.2529++ I [y3] - 359.2401+[1] 34880 I [b2] - 261.1598+[2]  7931 AFP R.HPFLYAPTILLW 590.3348+++ I [y7] - 421.7660++[1] 11471 AAR.Y L [y6] - 365.2239++[2]  5001 A [b6] - 365.1896++[3]  5001 L [y6] - 729.4406+[4]  3218 F [b3] - 382.1874+[5]  6536 A [b6] - 729.3719+[6]  3218 AFP R.TFQAITVTK.L 504.7898++ T [b6] - 662.3508+[1] 11241 T [y4] - 448.2766+[2]  7541 A [b4] - 448.2191+[3]  7541 AFP K.LTTLER.G 366.7162++ T [y4] - 518.2933+[1]  7836 L [b4] - 215.1390++[2]  4205 T [b2] - 215.1390+[3]  4205 AFP R.HPQLAVSVILR.V L[y2] - 288.2030+[1]  3781 I [y3] - 401.2871+[2]  2924 L [b4] - 476.2616+[3]  2647 AFP K.LGEYYLQNAFLV 631.6646+++ G [b2] - 171.1128+[1] 10790 AYTK.K Y [y3] - 411.2238+[2]  2303 F [b10] - 600.2902++[3]  1780 Y [b4] - 463.2187+[4]  2214 F [y7] - 421.2445++[6]  3072 PGH1 R.ILPSVPK.D 377.2471++ P [y5] - 527.3188+[1] 5340492   S [y4] - 430.2660+[5] 419777  P [y2] - 244.1656+[2] 4198508   P [y5] - 264.1630++[3] 2771328   L [b2] - 227.1754+[4] 2331263   PGH1 K.AEHPTWGDEQL 639.3026+++ E [b9] - 512.2120++[1] 64350 FQTTR.L P [b4] - 218.1030++[2] 38282 L [b11] - 632.7833++[3] 129128  G [y10] - 597.7911++[4] 19406 G [b7] - 779.3471+[5] 51467 T [y3] - 189.1108++[6] 10590 D [y9] - 569.2804++[7] 12460 L [y6] - 765.4254+[8]  6704 D [b8] - 447.6907++[9]  4893 P [b4] - 435.1987+[10]  8858 Q [y7] - 893.4839+[11]  6101 T [b5] - 268.6268++[12]  5456 T [b5] - 536.2463+[13]  5549 PGH1 R.LILIGETIK.I 500.3261++ G [y5] - 547.3086+[1]  7649 T [y3] - 361.2445+[2]  6680 E [y4] - 490.2871+[3]  5234 L [y7] - 773.4767+[4]  3342 PGH1 R.LQPFNEYR.K 533.7694++ N [b5] - 600.3140+[1] 25963 F [b4] - 486.2711+[2]  6915 E [y3] - 467.2249+[3] 15079 *QTRAP5500 data, all other peak areas are from Agilent 6490

Next, the top 2-10 transitions per peptide and up to 7 peptides per protein were selected for collision energy (CE) optimization on the Agilent 6490. Using Skyline or MassHunter Qual software, the optimized CE value for each transition was determined based on the peak area or signal to noise. The two transitions with the largest peak areas per peptide and at least two peptides per protein were chosen for the final MRM method. Substitutions of transitions with lower peak areas were made when a transition with a larger peak area had a high background level or had a low m/z value that has more potential for interference.

Lastly, the retention times of selected peptides were mapped using the same column and gradient as our established sMRM assay. The newly discovered analytes were subsequently added to the sMRM method and used in a further hypothesis-dependent discovery study described in Example 5 below.

The above method was typical for most proteins. However, in some cases, the differentially expressed peptide identified in the shotgun method did not uniquely identify a protein, for example, in protein families with high sequence identity. In these cases, a MRM method was developed for each family member. Also, let it be noted that, for any given protein, peptides in addition to those found to be significant and fragment ions not observed on the Orbitrap may have been included in MRM optimization and added to the final sMRM method if those yielded the best signal intensities.

Example 5. Study IV to Identify and Confirm Preterm Birth Biomarkers

A further hypothesis-dependent discovery study was performed with the scheduled MRM assay used in Examples 3 but now augmented with newly discovered analytes from the Example 4. Less robust transitions (from the original 1708 described in Example 1) were removed to improve analytical performance and make room for the newly discovered analytes. Samples included approximately 30 cases and 60 matched controls from each of three gestational periods (early, 17-22 weeks, middle, 23-25 weeks and late, 26-28 weeks). Log transformed peak areas for each transition were corrected for run order and batch effects by regression. The ability of each analyte to separate cases and controls was determined by calculating univariate AUC values from ROC curves. Ranked univariate AUC values (0.6 or greater) are reported for individual gestational age window sample sets (Tables 12, 13, 15) and a combination of the middle and late window (Table 14). Multivariate classifiers were built using different subsets of analytes (described below) by Lasso and Random Forest methods. Lasso significant transitions correspond to those with non-zero coefficients and Random Forest analyte ranking was determined by the Gini importance values (mean decrease in model accuracy if that variable is removed). We report all analytes with non-zero Lasso coefficients (Tables 16-32) and the top 30 analytes from each Random Forest analysis (Tables 33-49). Models were built considering the top univariate 32 or 100 analytes, the single best univariate analyte for the top 50 proteins or all analytes. Lastly 1000 rounds of bootstrap resampling were performed and the nonzero Lasso coefficients or Random Forest Gini importance values were summed for each analyte amongst panels with AUCs of 0.85 or greater.

TABLE 12 Early Window Individual Stats Transition Protein AUC ELIEELVNITQNQK_557.6_517.3 IL13_HUMAN 0.834 ITLPDFTGDLR_624.3_288.2 LBP_HUMAN 0.822 FLNWIK_410.7_560.3 HABP2_HUMAN 0.820 ITLPDFTGDLR_624.3_920.5 LBP_HUMAN 0.808 SFRPFVPR_335.9_635.3 LBP_HUMAN 0.800 LIQDAVTGLTVNGQITGDK_972.0_798.4 ITIH3_HUMAN 0.800 FSVVYAK_407.2_579.4 FETUA_HUMAN 0.796 ITGFLKPGK_320.9_429.3 LBP_HUMAN 0.796 AHYDLR_387.7_288.2 FETUA_HUMAN 0.796 FSVVYAK_407.2_381.2 FETUA_HUMAN 0.795 SFRPFVPR_335.9_272.2 LBP_HUMAN 0.795 DVLLLVHNLPQNLPGYFWYK_810.4_967.5 PSG9_HUMAN 0.794 ELIEELVNITQNQK_557.6_618.3 IL13_HUMAN 0.794 QALEEFQK_496.8_680.3 CO8B_HUMAN 0.792 DAGLSWGSAR_510.3_390.2 NEUR4_HUMAN 0.792 AHYDLR_387.7_566.3 FETUA_HUMAN 0.791 VFQFLEK_455.8_811.4 CO5_HUMAN 0.786 ITGFLKPGK_320.9_301.2 LBP_HUMAN 0.783 VFQFLEK_455.8_276.2 CO5_HUMAN 0.782 SLLQPNK_400.2_599.4 CO8A_HUMAN 0.781 VQTAHFK_277.5_431.2 CO8A_HUMAN 0.780 SDLEVAHYK_531.3_617.3 CO8B_HUMAN 0.777 SLLQPNK_400.2_358.2 CO8A_HUMAN 0.776 TLLPVSKPEIR_418.3_288.2 CO5_HUMAN 0.776 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 0.774 DISEVVTPR_508.3_787.4 CFAB_HUMAN 0.774 VSEADSSNADWVTK_754.9_533.3 CFAB_HUMAN 0.773 LSSPAVITDK_515.8_743.4 PLMN_HUMAN 0.773 VQEAHLTEDQIFYFPK_655.7_701.4 CO8G_HUMAN 0.772 DVLLLVHNLPQNLPGYFWYK_810.4_594.3 PSG9_HUMAN 0.771 ALVLELAK_428.8_672.4 INHBE_HUMAN 0.770 FLNWIK_410.7_561.3 HABP2_HUMAN 0.770 LSSPAVITDK_515.8_830.5 PLMN_HUMAN 0.769 LPNNVLQEK_527.8_844.5 AFAM_HUMAN 0.769 VSEADSSNADWVTK_754.9_347.2 CFAB_HUMAN 0.768 HTLNQIDEVK_598.8_951.5 FETUA_HUMAN 0.767 TTSDGGYSFK_531.7_860.4 INHA_HUMAN 0.761 YENYTSSFFIR_713.8_756.4 IL12B_HUMAN 0.760 HTLNQIDEVK_598.8_958.5 FETUA_HUMAN 0.760 DISEVVTPR_508.3_472.3 CFAB_HUMAN 0.760 LIQDAVTGLTVNGQITGDK_972.0_640.4 ITIH3_HUMAN 0.759 EAQLPVIENK_570.8_699.4 PLMN_HUMAN 0.759 SLPVSDSVLSGFEQR_810.9_836.4 CO8G_HUMAN 0.757 AVLHIGEK_289.5_348.7 THBG_HUMAN 0.755 GLQYAAQEGLLALQSELLR_1037.1_929.5 LBP_HUMAN 0.752 FLQEQGHR_338.8_497.3 CO8G_HUMAN 0.750 LPNNVLQEK_527.8_730.4 AFAM_HUMAN 0.750 AVLHIGEK_289.5_292.2 THBG_HUMAN 0.749 QLYGDTGVLGR_589.8_501.3 CO8G_HUMAN 0.748 WWGGQPLWITATK_772.4_929.5 ENPP2_HUMAN 0.747 NADYSYSVWK_616.8_769.4 CO5_HUMAN 0.746 GLQYAAQEGLLALQSELLR_1037.1_858.5 LBP_HUMAN 0.746 SLPVSDSVLSGFEQR_810.9_723.3 CO8G_HUMAN 0.745 IEEIAAK_387.2_531.3 CO5_HUMAN 0.743 TYLHTYESEI_628.3_908.4 ENPP2_HUMAN 0.742 WWGGQPLWITATK_772.4_373.2 ENPP2_HUMAN 0.742 FQLSETNR_497.8_605.3 PSG2_HUMAN 0.741 NIQSVNVK_451.3_674.4 GROA_HUMAN 0.741 TGVAVNKPAEFTVDAK_549.6_258.1 FLNA_HUMAN 0.740 LQGTLPVEAR_542.3_571.3 CO5_HUMAN 0.740 SGFSFGFK_438.7_732.4 CO8B_HUMAN 0.740 HELTDEELQSLFTNFANVVDK_817.1_906.5 AFAM_HUMAN 0.740 VQTAHFK_277.5_502.3 CO8A_HUMAN 0.739 YENYTSSFFIR_713.8_293.1 IL12B_HUMAN 0.739 AFTECCVVASQLR_770.9_574.3 CO5_HUMAN 0.736 EAQLPVIENK_570.8_329.2 PLMN_HUMAN 0.734 QALEEFQK_496.8_551.3 CO8B_HUMAN 0.734 DAQYAPGYDK_564.3_813.4 CFAB_HUMAN 0.734 TEFLSNYLTNVDDITLVPGTLGR_846.8_600.3 ENPP2_HUMAN 0.734 IAIDLFK_410.3_635.4 HEP2_HUMAN 0.733 TASDFITK_441.7_781.4 GELS_HUMAN 0.731 YEFLNGR_449.7_606.3 PLMN_HUMAN 0.731 TVQAVLTVPK_528.3_428.3 PEDF_HUMAN 0.731 LIENGYFHPVK_439.6_627.4 F13B_HUMAN 0.730 DALSSVQESQVAQQAR_573.0_672.4 APOC3_HUMAN 0.730 TVQAVLTVPK_528.3_855.5 PEDF_HUMAN 0.730 ALQDQLVLVAAK_634.9_289.2 ANGT_HUMAN 0.727 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 0.727 SDLEVAHYK_531.3_746.4 CO8B_HUMAN 0.726 FLPCENK_454.2_550.2 IL10_HUMAN 0.725 HPWIVHWDQLPQYQLNR_744.0_1047.0 KS6A3_HUMAN 0.725 AFTECCVVASQLR_770.9_673.4 CO5_HUMAN 0.725 YGLVTYATYPK_638.3_843.4 CFAB_HUMAN 0.724 TLEAQLTPR_514.8_685.4 HEP2_HUMAN 0.724 DAQYAPGYDK_564.3_315.1 CFAB_HUMAN 0.724 QGHNSVFLIK_381.6_260.2 HEMO_HUMAN 0.722 HELTDEELQSLFTNFANVVDK_817.1_854.4 AFAM_HUMAN 0.722 TLEAQLTPR_514.8_814.4 HEP2_HUMAN 0.721 IEEIAAK_387.2_660.4 CO5_HUMAN 0.721 HFQNLGK_422.2_527.2 AFAM_HUMAN 0.721 IAPQLSTEELVSLGEK_857.5_333.2 AFAM_HUMAN 0.721 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN 0.720 ALNHLPLEYNSALYSR_621.0_696.4 CO6_HUMAN 0.719 IAIDLFK_410.3_706.4 HEP2_HUMAN 0.719 FLQEQGHR_338.8_369.2 CO8G_HUMAN 0.719 ALQDQLVLVAAK_634.9_956.6 ANGT_HUMAN 0.718 IEGNLIFDPNNYLPK_874.0_414.2 APOB_HUMAN 0.717 YEFLNGR_449.7_293.1 PLMN_HUMAN 0.717 TASDFITK_441.7_710.4 GELS_HUMAN 0.716 DADPDTFFAK_563.8_825.4 AFAM_HUMAN 0.716 TLLPVSKPEIR_418.3_514.3 CO5_HUMAN 0.716 NADYSYSVWK_616.8_333.2 CO5_HUMAN 0.715 YGLVTYATYPK_638.3_334.2 CFAB_HUMAN 0.715 VNHVTLSQPK_374.9_459.3 B2MG_HUMAN 0.715 HYGGLTGLNK_530.3_759.4 PGAM1_HUMAN 0.714 DFHINLFQVLPWLK_885.5_400.2 CFAB_HUMAN 0.714 NCSFSIIYPVVIK_770.4_555.4 CRHBP_HUMAN 0.714 HPWIVHWDQLPQYQLNR_744.0_918.5 KS6A3_HUMAN 0.712 AQPVQVAEGSEPDGFWEALGGK_758.0_574.3 GELS_HUMAN 0.711 ALDLSLK_380.2_185.1 ITIH3_HUMAN 0.711 ALDLSLK_380.2_575.3 ITIH3_HUMAN 0.710 LDFHFSSDR_375.2_611.3 INHBC_HUMAN 0.709 TLNAYDHR_330.5_312.2 PAR3_HUMAN 0.707 EVFSKPISWEELLQ_852.9_260.2 FA40A_HUMAN 0.706 IAPQLSTEELVSLGEK_857.5_533.3 AFAM_HUMAN 0.704 LIENGYFHPVK_439.6_343.2 F13B_HUMAN 0.703 NFPSPVDAAFR_610.8_775.4 HEMO_HUMAN 0.703 QLYGDTGVLGR_589.8_345.2 CO8G_HUMAN 0.702 LYYGDDEK_501.7_563.2 CO8A_HUMAN 0.702 FQLSETNR_497.8_476.3 PSG2_HUMAN 0.701 TGVAVNKPAEFTVDAK_549.6_977.5 FLNA_HUMAN 0.700 IPGIFELGISSQSDR_809.9_679.3 CO8B_HUMAN 0.700 TLFIFGVTK_513.3_215.1 PSG4_HUMAN 0.699 YYGYTGAFR_549.3_450.3 TRFL_HUMAN 0.699 QVFAVQR_424.2_473.3 ELNE_HUMAN 0.699 AQPVQVAEGSEPDGFWEALGGK_758.0_623.4 GELS_HUMAN 0.699 DFNQFSSGEK_386.8_189.1 FETA_HUMAN 0.699 SVSLPSLDPASAK_636.4_473.3 APOB_HUMAN 0.699 GNGLTWAEK_488.3_634.3 C163B_HUMAN 0.698 LYYGDDEK_501.7_726.3 CO8A_HUMAN 0.698 NFPSPVDAAFR_610.8_959.5 HEMO_HUMAN 0.698 FAFNLYR_465.8_565.3 HEP2_HUMAN 0.697 SGFSFGFK_438.7_585.3 CO8B_HUMAN 0.696 DFHINLFQVLPWLK_885.5_543.3 CFAB_HUMAN 0.696 LQGTLPVEAR_542.3_842.5 CO5_HUMAN 0.694 GAVHVVVAETDYQSFAVLYLER_822.8_863.5 CO8G_HUMAN 0.694 TSESTGSLPSPFLR_739.9_716.4 PSMG1_HUMAN 0.694 YISPDQLADLYK_713.4_277.2 ENOA_HUMAN 0.694 ESDTSYVSLK_564.8_347.2 CRP_HUMAN 0.693 ILDDLSPR_464.8_587.3 ITIH4_HUMAN 0.693 VQEAHLTEDQIFYFPK_655.7_391.2 CO8G_HUMAN 0.692 SGVDLADSNQK_567.3_662.3 VGFR3_HUMAN 0.692 DTDTGALLFIGK_625.8_217.1 PEDF_HUMAN 0.692 HFQNLGK_422.2_285.1 AFAM_HUMAN 0.691 NNQLVAGYLQGPNVNLEEK_700.7_999.5 IL1RA_HUMAN 0.691 IPGIFELGISSQSDR_809.9_849.4 CO8B_HUMAN 0.691 ESDTSYVSLK_564.8_696.4 CRP_HUMAN 0.690 GAVHVVVAETDYQSFAVLYLER_822.8_580.3 CO8G_HUMAN 0.690 DADPDTFFAK_563.8_302.1 AFAM_HUMAN 0.690 LDFHFSSDR_375.2_464.2 INHBC_HUMAN 0.689 TLFIFGVTK_513.3_811.5 PSG4_HUMAN 0.688 DFNQFSSGEK_386.8_333.2 FETA_HUMAN 0.687 IQTHSTTYR_369.5_627.3 F13B_HUMAN 0.686 HYFIAAVER_553.3_658.4 FA8_HUMAN 0.686 VNHVTLSQPK_374.9_244.2 B2MG_HUMAN 0.686 DLHLSDVFLK_396.2_366.2 CO6_HUMAN 0.685 DPTFIPAPIQAK_433.2_556.3 ANGT_HUMAN 0.684 AGITIPR_364.2_272.2 IL17_HUMAN 0.684 IAQYYYTFK_598.8_884.4 F13B_HUMAN 0.684 SGVDLADSNQK_567.3_591.3 VGFR3_HUMAN 0.683 VEPLYELVTATDFAYSSTVR_754.4_549.3 CO8B_HUMAN 0.682 AGITIPR_364.2_486.3 IL17_HUMAN 0.682 YEVQGEVFTKPQLWP_911.0_293.1 CRP_HUMAN 0.681 APLTKPLK_289.9_357.2 CRP_HUMAN 0.681 YNSQLLSFVR_613.8_508.3 TFR1_HUMAN 0.681 ANDQYLTAAALHNLDEAVK_686.4_301.1 IL1A_HUMAN 0.681 IQTHSTTYR_369.5_540.3 F13B_HUMAN 0.681 IHPSYTNYR_575.8_598.3 PSG2_HUMAN 0.681 TEFLSNYLTNVDDITLVPGTLGR_846.8_699.4 ENPP2_HUMAN 0.681 DPTFIPAPIQAK_433.2_461.2 ANGT_HUMAN 0.679 FQSVFTVTR_542.8_623.4 C1QC_HUMAN 0.679 LQVNTPLVGASLLR_741.0_925.6 BPIA1_HUMAN 0.679 DEIPHNDIALLK_459.9_510.8 HABP2_HUMAN 0.678 HATLSLSIPR_365.6_272.2 VGFR3_HUMAN 0.678 EDTPNSVWEPAK_686.8_315.2 C1S_HUMAN 0.678 TGISPLALIK_506.8_741.5 APOB_HUMAN 0.678 ILPSVPK_377.2_244.2 PGH1_HUMAN 0.676 HATLSLSIPR_365.6_472.3 VGFR3_HUMAN 0.676 QGHNSVFLIK_381.6_520.4 HEMO_HUMAN 0.676 LPATEKPVLLSK_432.6_460.3 HYOU1_HUMAN 0.675 APLTKPLK_289.9_398.8 CRP_HUMAN 0.674 GVTGYFTFNLYLK_508.3_683.9 PSG5_HUMAN 0.673 TFLTVYWTPER_706.9_401.2 ICAM1_HUMAN 0.673 GDTYPAELYITGSILR_885.0_274.1 F13B_HUMAN 0.672 EDTPNSVWEPAK_686.8_630.3 C1S_HUMAN 0.672 SLDFTELDVAAEK_719.4_316.2 ANGT_HUMAN 0.672 VELAPLPSWQPVGK_760.9_342.2 ICAM1_HUMAN 0.671 GPGEDFR_389.2_322.2 PTGDS_HUMAN 0.670 TDAPDLPEENQAR_728.3_843.4 CO5_HUMAN 0.670 GVTGYFTFNLYLK_508.3_260.2 PSG5_HUMAN 0.669 FAFNLYR_465.8_712.4 HEP2_HUMAN 0.669 ITENDIQIALDDAK_779.9_873.5 APOB_HUMAN 0.669 ILNIFGVIK_508.8_790.5 TFR1_HUMAN 0.669 ISQGEADINIAFYQR_575.6_684.4 MMP8_HUMAN 0.668 GDTYPAELYITGSILR_885.0_1332.8 F13B_HUMAN 0.668 ELLESYIDGR_597.8_710.4 THRB_HUMAN 0.668 FTITAGSK_412.7_576.3 FABPL_HUMAN 0.667 ILDGGNK_358.7_490.2 CXCL5_HUMAN 0.667 GWVTDGFSSLK_598.8_854.4 APOC3_HUMAN 0.667 FSLVSGWGQLLDR_493.3_403.2 FA7_HUMAN 0.665 IHPSYTNYR_575.8_813.4 PSG2_HUMAN 0.665 ELLESYIDGR_597.8_839.4 THRB_HUMAN 0.665 SDGAKPGPR_442.7_213.6 COLI_HUMAN 0.664 IAQYYYTFK_598.8_395.2 F13B_HUMAN 0.664 SILFLGK_389.2_201.1 THBG_HUMAN 0.664 IEVNESGTVASSSTAVIVSAR_693.0_545.3 PAI1_HUMAN 0.664 VSAPSGTGHLPGLNPL_506.3_300.7 PSG3_HUMAN 0.664 LLAPSDSPEWLSFDVTGVVR_730.1_430.3 TGFB1_HUMAN 0.664 YYGYTGAFR_549.3_771.4 TRFL_HUMAN 0.663 TDAPDLPEENQAR_728.3_613.3 CO5_HUMAN 0.663 IEVIITLK_464.8_815.5 CXL11_HUMAN 0.662 ILPSVPK_377.2_227.2 PGH1_HUMAN 0.662 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 0.661 DYWSTVK_449.7_347.2 APOC3_HUMAN 0.661 IEGNLIFDPNNYLPK_874.0_845.5 APOB_HUMAN 0.661 WILTAAHTLYPK_471.9_407.2 C1R_HUMAN 0.661 WNFAYWAAHQPWSR_607.3_545.3 PRG2_HUMAN 0.661 SILFLGK_389.2_577.4 THBG_HUMAN 0.661 FSLVSGWGQLLDR_493.3_516.3 FA7_HUMAN 0.661 DTDTGALLFIGK_625.8_818.5 PEDF_HUMAN 0.661 SEYGAALAWEK_612.8_845.5 CO6_HUMAN 0.660 LWAYLTIQELLAK_781.5_371.2 ITIH1_HUMAN 0.660 LLEVPEGR_456.8_356.2 C1S_HUMAN 0.659 ITENDIQIALDDAK_779.9_632.3 APOB_HUMAN 0.659 LTTVDIVTLR_565.8_716.4 IL2RB_HUMAN 0.658 IEVIITLK_464.8_587.4 CXL11_HUMAN 0.658 QLGLPGPPDVPDHAAYHPF_676.7_299.2 ITIH4_HUMAN 0.658 TLAFVR_353.7_492.3 FA7_HUMAN 0.656 NSDQEIDFK_548.3_294.2 S10A5_HUMAN 0.656 YHFEALADTGISSEFYDNANDLLSK_940.8_874.5 CO8A_HUMAN 0.656 SEPRPGVLLR_375.2_454.3 FA7_HUMAN 0.655 FLPCENK_454.2_390.2 IL10_HUMAN 0.654 NCSFSIIYPVVIK_770.4_831.5 CRHBP_HUMAN 0.654 SLDFTELDVAAEK_719.4_874.5 ANGT_HUMAN 0.654 ILLLGTAVESAWGDEQSAFR_721.7_909.4 CXA1_HUMAN 0.653 SVSLPSLDPASAK_636.4_885.5 APOB_HUMAN 0.653 TGISPLALIK_506.8_654.5 APOB_HUMAN 0.653 YNQLLR_403.7_288.2 ENOA_HUMAN 0.653 YEVQGEVFTKPQLWP_911.0_392.2 CRP_HUMAN 0.652 VPGLYYFTYHASSR_554.3_720.3 C1QB_HUMAN 0.650 SLQNASAIESILK_687.4_589.4 IL3_HUMAN 0.650 WILTAAHTLYPK_471.9_621.4 C1R_HUMAN 0.650 GWVTDGFSSLK_598.8_953.5 APOC3_HUMAN 0.650 YGIEEHGK_311.5_599.3 CXA1_HUMAN 0.649 QDLGWK_373.7_503.3 TGFB3_HUMAN 0.649 DYWSTVK_449.7_620.3 APOC3_HUMAN 0.648 ALVLELAK_428.8_331.2 INHBE_HUMAN 0.647 QLGLPGPPDVPDHAAYHPF_676.7_263.1 ITIH4_HUMAN 0.646 SEYGAALAWEK_612.8_788.4 CO6_HUMAN 0.645 TFLTVYWTPER_706.9_502.3 ICAM1_HUMAN 0.644 FQSVFTVTR_542.8_722.4 C1QC_HUMAN 0.643 DPNGLPPEAQK_583.3_669.4 RET4_HUMAN 0.642 ETLLQDFR_511.3_322.2 AMBP_HUMAN 0.642 IIEVEEEQEDPYLNDR_996.0_777.4 FBLN1_HUMAN 0.641 ELCLDPK_437.7_359.2 IL8_HUMAN 0.641 TPSAAYLWVGTGASEAEK_919.5_849.4 GELS_HUMAN 0.641 NQSPVLEPVGR_598.3_866.5 KS6A3_HUMAN 0.641 FNAVLTNPQGDYDTSTGK_964.5_333.2 C1QC_HUMAN 0.641 LLEVPEGR_456.8_686.4 C1S_HUMAN 0.641 FFQYDTWK_567.8_840.4 IGF2_HUMAN 0.640 SPEAEDPLGVER_649.8_670.4 Z512B_HUMAN 0.639 SEPRPGVLLR_375.2_654.4 FA7_HUMAN 0.639 SGAQATWTELPWPHEK_613.3_793.4 HEMO_HUMAN 0.638 YSHYNER_323.5_581.3 HABP2_HUMAN 0.638 YHFEALADTGISSEFYDNANDLLSK_940.8_301.1 CO8A_HUMAN 0.637 DLHLSDVFLK_396.2_260.2 CO6_HUMAN 0.637 YSHYNER_323.5_418.2 HABP2_HUMAN 0.637 YYLQGAK_421.7_327.1 ITIH4_HUMAN 0.636 EVPLSALTNILSAQLISHWK_740.8_996.6 PAI1_HUMAN 0.636 VPGLYYFTYHASSR_554.3_420.2 C1QB_HUMAN 0.636 AALAAFNAQNNGSNFQLEEISR_789.1_746.4 FETUA_HUMAN 0.636 ETLLQDFR_511.3_565.3 AMBP_HUMAN 0.635 IVLSLDVPIGLLQILLEQAR_735.1_503.3 UCN2_HUMAN 0.635 ENPAVIDFELAPIVDLVR_670.7_811.5 CO6_HUMAN 0.635 LQLSETNR_480.8_355.2 PSG8_HUMAN 0.635 DPDQTDGLGLSYLSSHIANVER_796.4_456.2 GELS_HUMAN 0.635 NVNQSLLELHK_432.2_656.3 FRIH_HUMAN 0.634 EIGELYLPK_531.3_633.4 AACT_HUMAN 0.634 SPEQQETVLDGNLIIR_906.5_699.3 ITIH4_HUMAN 0.634 NKPGVYTDVAYYLAWIR_677.0_545.3 FA12_HUMAN 0.632 QNYHQDSEAAINR_515.9_544.3 FRIH_HUMAN 0.632 EKPAGGIPVLGSLVNTVLK_631.4_930.6 BPIB1_HUMAN 0.632 VTFEYR_407.7_614.3 CRHBP_HUMAN 0.630 DLPHITVDR_533.3_490.3 MMP7_HUMAN 0.630 VEHSDLSFSK_383.5_234.1 B2MG_HUMAN 0.630 ENPAVIDFELAPIVDLVR_670.7_601.4 CO6_HUMAN 0.630 YGFYTHVFR_397.2_659.4 THRB_HUMAN 0.629 ILDDLSPR_464.8_702.3 ITIH4_HUMAN 0.629 DPNGLPPEAQK_583.3_497.2 RET4_HUMAN 0.629 GSLVQASEANLQAAQDFVR_668.7_806.4 ITIH1_HUMAN 0.629 FLYHK_354.2_447.2 AMBP_HUMAN 0.627 FNAVLTNPQGDYDTSTGK_964.5_262.1 C1QC_HUMAN 0.627 LQDAGVYR_461.2_680.3 PD1L1_HUMAN 0.627 INPASLDK_429.2_630.4 C163A_HUMAN 0.626 LEEHYELR_363.5_580.3 PAI2_HUMAN 0.625 VEHSDLSFSK_383.5_468.2 B2MG_HUMAN 0.624 TSDQIHFFFAK_447.6_659.4 ANT3_HUMAN 0.624 ATLSAAPSNPR_542.8_570.3 CXCL2_HUMAN 0.624 YGFYTHVFR_397.2_421.3 THRB_HUMAN 0.624 EANQSTLENFLER_775.9_678.4 IL4_HUMAN 0.623 GQQPADVTGTALPR_705.9_314.2 CSF1_HUMAN 0.623 VELAPLPSWQPVGK_760.9_400.3 ICAM1_HUMAN 0.622 GEVTYTTSQVSK_650.3_750.4 EGLN_HUMAN 0.622 SLQAFVAVAAR_566.8_487.3 IL23A_HUMAN 0.622 HYGGLTGLNK_530.3_301.1 PGAM1_HUMAN 0.622 GPEDQDISISFAWDK_854.4_753.4 DEF4_HUMAN 0.622 YVVISQGLDKPR_458.9_400.3 LRP1_HUMAN 0.621 LWAYLTIQELLAK_781.5_300.2 ITIH1_HUMAN 0.621 SGAQATWTELPWPHEK_613.3_510.3 HEMO_HUMAN 0.621 GTAEWLSFDVTDTVR_848.9_952.5 TGFB3_HUMAN 0.621 FFQYDTWK_567.8_712.3 IGF2_HUMAN 0.621 AHQLAIDTYQEFEETYIPK_766.0_634.4 CSH_HUMAN 0.620 LPATEKPVLLSK_432.6_347.2 HYOU1_HUMAN 0.620 NIQSVNVK_451.3_546.3 GROA_HUMAN 0.620 TAVTANLDIR_537.3_288.2 CHL1_HUMAN 0.619 WSAGLTSSQVDLYIPK_883.0_515.3 CBG_HUMAN 0.616 QINSYVK_426.2_496.3 CBG_HUMAN 0.616 GFQALGDAADIR_617.3_288.2 TIMP1_HUMAN 0.615 WNFAYWAAHQPWSR_607.3_673.3 PRG2_HUMAN 0.615 NEIWYR_440.7_357.2 FA12_HUMAN 0.615 VLEPTLK_400.3_587.3 VTDB_HUMAN 0.614 YYLQGAK_421.7_516.3 ITIH4_HUMAN 0.614 ALNSIIDVYHK_424.9_774.4 S10A8_HUMAN 0.614 ETPEGAEAKPWYEPIYLGGVFQLEK_951.1_877.5 TNFA_HUMAN 0.614 LNIGYIEDLK_589.3_837.4 PAI2_HUMAN 0.614 NVNQSLLELHK_432.2_543.3 FRIH_HUMAN 0.613 ILLLGTAVESAWGDEQSAFR_721.7_910.6 CXA1_HUMAN 0.613 AALAAFNAQNNGSNFQLEEISR_789.1_633.3 FETUA_HUMAN 0.613 VLEPTLK_400.3_458.3 VTDB_HUMAN 0.613 VGEYSLYIGR_578.8_708.4 SAMP_HUMAN 0.613 DIPHWLNPTR_416.9_373.2 PAPP1_HUMAN 0.612 NEIVFPAGILQAPFYTR_968.5_357.2 ECE1_HUMAN 0.612 AEHPTWGDEQLFQTTR_639.3_765.4 PGH1_HUMAN 0.612 VEPLYELVTATDFAYSSTVR_754.4_712.4 CO8B_HUMAN 0.611 DEIPHNDIALLK_459.9_260.2 HABP2_HUMAN 0.611 QINSYVK_426.2_610.3 CBG_HUMAN 0.610 SWNEPLYHLVTEVR_581.6_614.3 PRL_HUMAN 0.610 YGIEEHGK_311.5_341.2 CXA1_HUMAN 0.610 FGFGGSTDSGPIR_649.3_946.5 ADA12_HUMAN 0.610 ANDQYLTAAALHNLDEAVK_686.4_317.2 IL1A_HUMAN 0.610 VRPQQLVK_484.3_609.4 ITIH4_HUMAN 0.609 IPKPEASFSPR_410.2_506.3 ITIH4_HUMAN 0.609 SPEQQETVLDGNLIIR_906.5_685.4 ITIH4_HUMAN 0.609 DDLYVSDAFHK_655.3_704.3 ANT3_HUMAN 0.609 ELPEHTVK_476.8_347.2 VTDB_HUMAN 0.609 FLYHK_354.2_284.2 AMBP_HUMAN 0.608 QRPPDLDTSSNAVDLLFFTDESGDSR_961.5_262.2 C1R_HUMAN 0.608 DPDQTDGLGLSYLSSHIANVER_796.4_328.1 GELS_HUMAN 0.608 NEIWYR_440.7_637.4 FA12_HUMAN 0.607 LQLSETNR_480.8_672.4 PSG8_HUMAN 0.606 GQVPENEANVVITTLK_571.3_462.3 CADH1_HUMAN 0.606 FTGSQPFGQGVEHATANK_626.0_521.2 TSP1_HUMAN 0.605 LEPLYSASGPGLRPLVIK_637.4_260.2 CAA60698 0.605 QRPPDLDTSSNAVDLLFFTDESGDSR_961.5_866.3 C1R_HUMAN 0.604 LTTVDIVTLR_565.8_815.5 IL2RB_HUMAN 0.604 TSDQIHFFFAK_447.6_512.3 ANT3_HUMAN 0.604 IQHPFTVEEFVLPK_562.0_861.5 PZP_HUMAN 0.603 NKPGVYTDVAYYLAWIR_677.0_821.5 FA12_HUMAN 0.603 TEQAAVAR_423.2_615.4 FA12_HUMAN 0.603 EIGELYLPK_531.3_819.5 AACT_HUMAN 0.602 LFYADHPFIFLVR_546.6_647.4 SERPH_HUMAN 0.602 AEHPTWGDEQLFQTTR_639.3_569.3 PGH1_HUMAN 0.601 TSYQVYSK_488.2_787.4 C163A_HUMAN 0.601 YTTEIIK_434.2_704.4 C1R_HUMAN 0.601 NVIQISNDLENLR_509.9_402.3 LEP_HUMAN 0.600 AFLEVNEEGSEAAASTAVVIAGR_764.4_685.4 ANT3_HUMAN 0.600

TABLE 13 Middle Window Individual Stats Transition Protein AUC SEYGAALAWEK_612.8_788.4 CO6_HUMAN 0.738 VFQFLEK_455.8_811.4 CO5_HUMAN 0.709 ALNHLPLEYNSALYSR_621.0_696.4 CO6_HUMAN 0.705 SLDFTELDVAAEK_719.4_316.2 ANGT_HUMAN 0.692 VEHSDLSFSK_383.5_234.1 B2MG_HUMAN 0.686 LLAPSDSPEWLSFDVTGVVR_730.1_430.3 TGFB1_HUMAN 0.683 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 0.683 VLEPTLK_400.3_458.3 VTDB_HUMAN 0.681 LHEAFSPVSYQHDLALLR_699.4_251.2 FA12_HUMAN 0.681 SEYGAALAWEK_612.8_845.5 CO6_HUMAN 0.679 YGIEEHGK_311.5_599.3 CXA1_HUMAN 0.677 ALQDQLVLVAAK_634.9_289.2 ANGT_HUMAN 0.675 VLEPTLK_400.3_587.3 VTDB_HUMAN 0.667 VNHVTLSQPK_374.9_244.2 B2MG_HUMAN 0.665 IEEIAAK_387.2_660.4 CO5_HUMAN 0.664 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN 0.664 TLLPVSKPEIR_418.3_514.3 CO5_HUMAN 0.662 ALQDQLVLVAAK_634.9_956.6 ANGT_HUMAN 0.661 TLAFVR_353.7_492.3 FA7_HUMAN 0.661 SEPRPGVLLR_375.2_654.4 FA7_HUMAN 0.658 VEHSDLSFSK_383.5_468.2 B2MG_HUMAN 0.653 DPTFIPAPIQAK_433.2_461.2 ANGT_HUMAN 0.653 QGHNSVFLIK_381.6_260.2 HEMO_HUMAN 0.650 SLDFTELDVAAEK_719.4_874.5 ANGT_HUMAN 0.650 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN 0.649 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 0.647 SLQAFVAVAAR_566.8_804.5 IL23A_HUMAN 0.646 AQPVQVAEGSEPDGFWEALGGK_758.0_574.3 GELS_HUMAN 0.644 QGHNSVFLIK_381.6_520.4 HEMO_HUMAN 0.644 VNHVTLSQPK_374.9_459.3 B2MG_HUMAN 0.643 DLHLSDVFLK_396.2_260.2 CO6_HUMAN 0.643 TEQAAVAR_423.2_615.4 FA12_HUMAN 0.643 GPITSAAELNDPQSILLR_632.4_826.5 EGLN_HUMAN 0.643 HFQNLGK_422.2_527.2 AFAM_HUMAN 0.642 TEQAAVAR_423.2_487.3 FA12_HUMAN 0.642 AVDIPGLEAATPYR_736.9_399.2 TENA_HUMAN 0.642 TLFIFGVTK_513.3_811.5 PSG4_HUMAN 0.642 DLHLSDVFLK_396.2_366.2 CO6_HUMAN 0.641 AFTECCVVASQLR_770.9_574.3 CO5_HUMAN 0.640 EVFSKPISWEELLQ_852.9_376.2 FA40A_HUMAN 0.639 DPTFIPAPIQAK_433.2_556.3 ANGT_HUMAN 0.639 FSLVSGWGQLLDR_493.3_403.2 FA7_HUMAN 0.638 HYINLITR_515.3_301.1 NPY_HUMAN 0.637 HFQNLGK_422.2_285.1 AFAM_HUMAN 0.637 VPLALFALNR_557.3_620.4 PEPD_HUMAN 0.636 IHPSYTNYR_575.8_813.4 PSG2_HUMAN 0.635 IEEIAAK_387.2_531.3 CO5_HUMAN 0.635 GEVTYTTSQVSK_650.3_750.4 EGLN_HUMAN 0.634 DFNQFSSGEK_386.8_333.2 FETA_HUMAN 0.634 VVGGLVALR_442.3_784.5 FA12_HUMAN 0.634 SDGAKPGPR_442.7_459.2 COLI_HUMAN 0.634 DVLLLVHNLPQNLTGHIWYK_791.8_310.2 PSG7_HUMAN 0.634 TLLPVSKPEIR_418.3_288.2 CO5_HUMAN 0.633 NKPGVYTDVAYYLAWIR_677.0_821.5 FA12_HUMAN 0.630 QVFAVQR_424.2_473.3 ELNE_HUMAN 0.630 NHYTESISVAK_624.8_415.2 NEUR1_HUMAN 0.630 IAPQLSTEELVSLGEK_857.5_333.2 AFAM_HUMAN 0.629 IHPSYTNYR_575.8_598.3 PSG2_HUMAN 0.627 EVFSKPISWEELLQ_852.9_260.2 FA40A_HUMAN 0.627 SILFLGK_389.2_201.1 THBG_HUMAN 0.626 IEVIITLK_464.8_587.4 CXL11_HUMAN 0.625 VVGGLVALR_442.3_685.4 FA12_HUMAN 0.624 VVLSSGSGPGLDLPLVLGLPLQLK_791.5_598.4 SHBG_HUMAN 0.624 FGFGGSTDSGPIR_649.3_946.5 ADA12_HUMAN 0.623 VVLSSGSGPGLDLPLVLGLPLQLK_791.5_768.5 SHBG_HUMAN 0.622 YGIEEHGK_311.5_341.2 CXA1_HUMAN 0.621 LHEAFSPVSYQHDLALLR_699.4_380.2 FA12_HUMAN 0.621 AHYDLR_387.7_566.3 FETUA_HUMAN 0.620 FSVVYAK_407.2_381.2 FETUA_HUMAN 0.618 ALALPPLGLAPLLNLWAKPQGR_770.5_256.2 SHBG_HUMAN 0.618 YENYTSSFFIR_713.8_293.1 IL12B_HUMAN 0.617 VELAPLPSWQPVGK_760.9_342.2 ICAM1_HUMAN 0.617 SILFLGK_389.2_577.4 THBG_HUMAN 0.616 ILPSVPK_377.2_227.2 PGH1_HUMAN 0.615 IPSNPSHR_303.2_496.3 FBLN3_HUMAN 0.615 HYFIAAVER_553.3_301.1 FA8_HUMAN 0.615 FSVVYAK_407.2_579.4 FETUA_HUMAN 0.613 VFQFLEK_455.8_276.2 CO5_HUMAN 0.613 IAPQLSTEELVSLGEK_857.5_533.3 AFAM_HUMAN 0.613 ILPSVPK_377.2_244.2 PGH1_HUMAN 0.613 NKPGVYTDVAYYLAWIR_677.0_545.3 FA12_HUMAN 0.613 WSAGLTSSQVDLYIPK_883.0_515.3 CBG_HUMAN 0.612 TPSAAYLWVGTGASEAEK_919.5_849.4 GELS_HUMAN 0.612 ALALPPLGLAPLLNLWAKPQGR_770.5_457.3 SHBG_HUMAN 0.612 QLGLPGPPDVPDHAAYHPF_676.7_299.2 ITIH4_HUMAN 0.612 ILDDLSPR_464.8_587.3 ITIH4_HUMAN 0.611 VELAPLPSWQPVGK_760.9_400.3 ICAM1_HUMAN 0.611 DADPDTFFAK_563.8_825.4 AFAM_HUMAN 0.611 NHYTESISVAK_624.8_252.1 NEUR1_HUMAN 0.611 SEPRPGVLLR_375.2_454.3 FA7_HUMAN 0.611 LNIGYIEDLK_589.3_950.5 PAI2_HUMAN 0.611 ANLINNIFELAGLGK_793.9_299.2 LCAP_HUMAN 0.609 LTTVDIVTLR_565.8_716.4 IL2RB_HUMAN 0.608 TQILEWAAER_608.8_761.4 EGLN_HUMAN 0.608 NEPEETPSIEK_636.8_573.3 SOX5_HUMAN 0.608 AQPVQVAEGSEPDGFWEALGGK_758.0_623.4 GELS_HUMAN 0.607 LQVNTPLVGASLLR_741.0_925.6 BPIA1_HUMAN 0.607 VPSHAVVAR_312.5_345.2 TRFL_HUMAN 0.607 SLQNASAIESILK_687.4_860.5 IL3_HUMAN 0.607 GVTGYFTFNLYLK_508.3_260.2 PSG5_HUMAN 0.605 DFNQFSSGEK_386.8_189.1 FETA_HUMAN 0.605 QLGLPGPPDVPDHAAYHPF_676.7_263.1 ITIH4_HUMAN 0.605 TLEAQLTPR_514.8_814.4 HEP2_HUMAN 0.604 AFTECCVVASQLR_770.9_673.4 CO5_HUMAN 0.604 LTTVDIVTLR_565.8_815.5 IL2RB_HUMAN 0.604 TLNAYDHR_330.5_312.2 PAR3_HUMAN 0.603 LWAYLTIQELLAK_781.5_300.2 ITIH1_HUMAN 0.603 GGLFADIASHPWQAAIFAK_667.4_375.2 TPA_HUMAN 0.603 IPSNPSHR_303.2_610.3 FBLN3_HUMAN 0.603 TDAPDLPEENQAR_728.3_843.4 CO5_HUMAN 0.603 SPQAFYR_434.7_684.4 REL3_HUMAN 0.602 SSNNPHSPIVEEFQVPYNK_729.4_261.2 C1S_HUMAN 0.601 AHYDLR_387.7_288.2 FETUA_HUMAN 0.600 DGSPDVTTADIGANTPDATK_973.5_844.4 PGRP2_HUMAN 0.600 SPQAFYR_434.7_556.3 REL3_HUMAN 0.600

TABLE 14 Middle Late Individual Stats Transition Protein AUC ALNHLPLEYNSALYSR_621.0_696.4 CO6_HUMAN 0.656 VPLALFALNR_557.3_620.4 PEPD_HUMAN 0.655 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 0.652 AVYEAVLR_460.8_587.4 PEPD_HUMAN 0.649 SEPRPGVLLR_375.2_654.4 FA7_HUMAN 0.644 VFQFLEK_455.8_811.4 CO5_HUMAN 0.643 AQPVQVAEGSEPDGFWEALGGK_758.0_574.3 GELS_HUMAN 0.640 TLAFVR_353.7_492.3 FA7_HUMAN 0.639 TEQAAVAR_423.2_615.4 FA12_HUMAN 0.637 YGIEEHGK_311.5_599.3 CXA1_HUMAN 0.637 TEQAAVAR_423.2_487.3 FA12_HUMAN 0.633 QINSYVK_426.2_496.3 CBG_HUMAN 0.633 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 0.633 SEYGAALAWEK_612.8_845.5 CO6_HUMAN 0.633 ALQDQLVLVAAK_634.9_956.6 ANGT_HUMAN 0.628 VLEPTLK_400.3_587.3 VTDB_HUMAN 0.628 DFNQFSSGEK_386.8_333.2 FETA_HUMAN 0.628 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 0.628 LIEIANHVDK_384.6_498.3 ADA12_HUMAN 0.626 QINSYVK_426.2_610.3 CBG_HUMAN 0.625 SLDFTELDVAAEK_719.4_316.2 ANGT_HUMAN 0.625 DPTFIPAPIQAK_433.2_461.2 ANGT_HUMAN 0.625 AVYEAVLR_460.8_750.4 PEPD_HUMAN 0.623 YENYTSSFFIR_713.8_756.4 IL12B_HUMAN 0.623 SEYGAALAWEK_612.8_788.4 CO6_HUMAN 0.623 WSAGLTSSQVDLYIPK_883.0_515.3 CBG_HUMAN 0.622 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN 0.622 ALQDQLVLVAAK_634.9_289.2 ANGT_HUMAN 0.621 SLQAFVAVAAR_566.8_804.5 IL23A_HUMAN 0.621 DPTFIPAPIQAK_433.2_556.3 ANGT_HUMAN 0.620 FGFGGSTDSGPIR_649.3_946.5 ADA12_HUMAN 0.619 VLEPTLK_400.3_458.3 VTDB_HUMAN 0.619 SLDFTELDVAAEK_719.4_874.5 ANGT_HUMAN 0.618 EVFSKPISWEELLQ_852.9_376.2 FA40A_HUMAN 0.618 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 0.618 TPSAAYLWVGTGASEAEK_919.5_849.4 GELS_HUMAN 0.615 LHEAFSPVSYQHDLALLR_699.4_251.2 FA12_HUMAN 0.615 TLEAQLTPR_514.8_685.4 HEP2_HUMAN 0.613 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN 0.612 GYQELLEK_490.3_631.4 FETA_HUMAN 0.612 VPLALFALNR_557.3_917.6 PEPD_HUMAN 0.611 DLHLSDVFLK_396.2_260.2 CO6_HUMAN 0.611 LTTVDIVTLR_565.8_815.5 IL2RB_HUMAN 0.608 WSAGLTSSQVDLYIPK_883.0_357.2 CBG_HUMAN 0.608 ITQDAQLK_458.8_702.4 CBG_HUMAN 0.608 NIQSVNVK_451.3_674.4 GROA_HUMAN 0.607 ALEQDLPVNIK_620.4_570.4 CNDP1_HUMAN 0.607 TLNAYDHR_330.5_312.2 PAR3_HUMAN 0.606 LWAYLTIQELLAK_781.5_300.2 ITIH1_HUMAN 0.606 VVGGLVALR_442.3_784.5 FA12_HUMAN 0.605 AQPVQVAEGSEPDGFWEALGGK_758.0_623.4 GELS_HUMAN 0.603 SVVLIPLGAVDDGEHSQNEK_703.0_798.4 CNDP1_HUMAN 0.603 SETEIHQGFQHLHQLFAK_717.4_318.1 CBG_HUMAN 0.603 LLAPSDSPEWLSFDVTGVVR_730.1_430.3 TGFB1_HUMAN 0.603 IEVIITLK_464.8_587.4 CXL11_HUMAN 0.602 ITQDAQLK_458.8_803.4 CBG_HUMAN 0.602 AEIEYLEK_497.8_552.3 LYAM1_HUMAN 0.601 AVDIPGLEAATPYR_736.9_399.2 TENA_HUMAN 0.601 LTTVDIVTLR_565.8_716.4 IL2RB_HUMAN 0.600 WWGGQPLWITATK_772.4_929.5 ENPP2_HUMAN 0.600

TABLE 15 Late Window Individual Stats Transition Protein AUC AVYEAVLR_460.8_587.4 PEPD_HUMAN 0.724 AEIEYLEK_497.8_552.3 LYAM1_HUMAN 0.703 QINSYVK_426.2_496.3 CBG_HUMAN 0.695 AVYEAVLR_460.8_750.4 PEPD_HUMAN 0.693 AALAAFNAQNNGSNFQLEEISR_789.1_746.4 FETUA_HUMAN 0.684 QINSYVK_426.2_610.3 CBG_HUMAN 0.681 VPLALFALNR_557.3_620.4 PEPD_HUMAN 0.678 VGVISFAQK_474.8_580.3 TFR2_HUMAN 0.674 TGVAVNKPAEFTVDAK_549.6_258.1 FLNA_HUMAN 0.670 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 0.670 LIEIANHVDK_384.6_498.3 ADA12_HUMAN 0.660 SGVDLADSNQK_567.3_662.3 VGFR3_HUMAN 0.660 TSYQVYSK_488.2_787.4 C163A_HUMAN 0.657 ITQDAQLK_458.8_702.4 CBG_HUMAN 0.652 YYGYTGAFR_549.3_450.3 TRFL_HUMAN 0.650 ALEQDLPVNIK_620.4_798.5 CNDP1_HUMAN 0.650 VFQYIDLHQDEFVQTLK_708.4_375.2 CNDP1_HUMAN 0.650 SGVDLADSNQK_567.3_591.3 VGFR3_HUMAN 0.648 YENYTSSFFIR_713.8_756.4 IL12B_HUMAN 0.647 VLSSIEQK_452.3_691.4 1433S_HUMAN 0.647 YSHYNER_323.5_418.2 HABP2_HUMAN 0.646 ILDGGNK_358.7_603.3 CXCL5_HUMAN 0.645 GTYLYNDCPGPGQDTDCR_697.0_666.3 TNR1A_HUMAN 0.645 AEIEYLEK_497.8_389.2 LYAM1_HUMAN 0.645 TLPFSR_360.7_506.3 LYAM1_HUMAN 0.645 DEIPHNDIALLK_459.9_510.8 HABP2_HUMAN 0.644 ALEQDLPVNIK_620.4_570.4 CNDP1_HUMAN 0.644 SPEAEDPLGVER_649.8_314.1 Z512B_HUMAN 0.644 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 0.642 TASDFITK_441.7_781.4 GELS_HUMAN 0.641 SETEIHQGFQHLHQLFAK_717.4_447.2 CBG_HUMAN 0.640 SPQAFYR_434.7_556.3 REL3_HUMAN 0.639 TAVTANLDIR_537.3_288.2 CHL1_HUMAN 0.636 VPLALFALNR_557.3_917.6 PEPD_HUMAN 0.636 YISPDQLADLYK_713.4_277.2 ENOA_HUMAN 0.633 SETEIHQGFQHLHQLFAK_717.4_318.1 CBG_HUMAN 0.633 SEPRPGVLLR_375.2_654.4 FA7_HUMAN 0.633 GYQELLEK_490.3_631.4 FETA_HUMAN 0.633 AYSDLSR_406.2_375.2 SAMP_HUMAN 0.633 SVVLIPLGAVDDGEHSQNEK_703.0_798.4 CNDP1_HUMAN 0.632 TLEAQLTPR_514.8_685.4 HEP2_HUMAN 0.631 WSAGLTSSQVDLYIPK_883.0_515.3 CBG_HUMAN 0.631 TEQAAVAR_423.2_615.4 FA12_HUMAN 0.628 AQPVQVAEGSEPDGFWEALGGK_758.0_574.3 GELS_HUMAN 0.626 AGITIPR_364.2_486.3 IL17_HUMAN 0.626 AEVIWTSSDHQVLSGK_586.3_300.2 PD1L1_HUMAN 0.625 TEQAAVAR_423.2_487.3 FA12_HUMAN 0.625 NHYTESISVAK_624.8_415.2 NEUR1_HUMAN 0.625 WSAGLTSSQVDLYIPK_883.0_357.2 CBG_HUMAN 0.623 YSHYNER_323.5_581.3 HABP2_HUMAN 0.623 DFNQFSSGEK_386.8_333.2 FETA_HUMAN 0.621 NIQSVNVK_451.3_674.4 GROA_HUMAN 0.620 SVVLIPLGAVDDGEHSQNEK_703.0_286.2 CNDP1_HUMAN 0.620 TLAFVR_353.7_492.3 FA7_HUMAN 0.619 AVDIPGLEAATPYR_736.9_286.1 TENA_HUMAN 0.619 TEFLSNYLTNVDDITLVPGTLGR_846.8_600.3 ENPP2_HUMAN 0.618 YWGVASFLQK_599.8_849.5 RET4_HUMAN 0.618 TPSAAYLWVGTGASEAEK_919.5_428.2 GELS_HUMAN 0.618 DPNGLPPEAQK_583.3_669.4 RET4_HUMAN 0.617 TYLHTYESEI_628.3_908.4 ENPP2_HUMAN 0.616 SPQAFYR_434.7_684.4 REL3_HUMAN 0.616 TPSAAYLWVGTGASEAEK_919.5_849.4 GELS_HUMAN 0.615 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 0.615 IEVNESGTVASSSTAVIVSAR_693.0_545.3 PAI1_HUMAN 0.615 LTTVDIVTLR_565.8_815.5 IL2RB_HUMAN 0.615 LWAYLTIQELLAK_781.5_371.2 ITIH1_HUMAN 0.613 SYTITGLQPGTDYK_772.4_352.2 FINC_HUMAN 0.612 GAVHVVVAETDYQSFAVLYLER_822.8_863.5 CO8G_HUMAN 0.612 FQLPGQK_409.2_276.1 PSG1_HUMAN 0.612 ILDGGNK_358.7_490.2 CXCL5_HUMAN 0.611 DYWSTVK_449.7_620.3 APOC3_HUMAN 0.611 AGLLRPDYALLGHR_518.0_595.4 PGRP2_HUMAN 0.611 ALNFGGIGVVVGHELTHAFDDQGR_837.1_360.2 ECE1_HUMAN 0.611 GYQELLEK_490.3_502.3 FETA_HUMAN 0.611 HATLSLSIPR_365.6_472.3 VGFR3_HUMAN 0.610 SVPVTKPVPVTKPITVTK_631.1_658.4 Z512B_HUMAN 0.610 FQLPGQK_409.2_429.2 PSG1_HUMAN 0.610 IYLQPGR_423.7_329.2 ITIH2_HUMAN 0.610 TLNAYDHR_330.5_312.2 PAR3_HUMAN 0.609 DPNGLPPEAQK_583.3_497.2 RET4_HUMAN 0.609 FGFGGSTDSGPIR_649.3_946.5 ADA12_HUMAN 0.609 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 0.608 GAVHVVVAETDYQSFAVLYLER_822.8_580.3 CO8G_HUMAN 0.608 VPSHAVVAR_312.5_515.3 TRFL_HUMAN 0.608 YWGVASFLQK_599.8_350.2 RET4_HUMAN 0.608 EWVAIESDSVQPVPR_856.4_468.3 CNDP1_HUMAN 0.607 LQDAGVYR_461.2_680.3 PD1L1_HUMAN 0.607 DLYHYITSYVVDGEIIIYGPAYSGR_955.5_650.3 PSG1_HUMAN 0.607 LWAYLTIQELLAK_781.5_300.2 ITIH1_HUMAN 0.606 ITENDIQIALDDAK_779.9_632.3 APOB_HUMAN 0.606 SYTITGLQPGTDYK_772.4_680.3 FINC_HUMAN 0.606 FFQYDTWK_567.8_712.3 IGF2_HUMAN 0.605 IYLQPGR_423.7_570.3 ITIH2_HUMAN 0.605 YNQLLR_403.7_529.4 ENOA_HUMAN 0.605 WWGGQPLWITATK_772.4_929.5 ENPP2_HUMAN 0.605 WWGGQPLWITATK_772.4_373.2 ENPP2_HUMAN 0.605 TASDFITK_441.7_710.4 GELS_HUMAN 0.605 EWVAIESDSVQPVPR_856.4_486.2 CNDP1_HUMAN 0.605 YEFLNGR_449.7_606.3 PLMN_HUMAN 0.604 SNPVTLNVLYGPDLPR_585.7_654.4 PSG6_HUMAN 0.604 ITQDAQLK_458.8_803.4 CBG_HUMAN 0.603 LTTVDIVTLR_565.8_716.4 IL2RB_HUMAN 0.602 FNAVLTNPQGDYDTSTGK_964.5_262.1 C1QC_HUMAN 0.602 ITGFLKPGK_320.9_301.2 LBP_HUMAN 0.601 DYWSTVK_449.7_347.2 APOC3_HUMAN 0.601 DPTFIPAPIQAK_433.2_556.3 ANGT_HUMAN 0.601 GWVTDGFSSLK_598.8_953.5 APOC3_HUMAN 0.601 YYGYTGAFR_549.3_771.4 TRFL_HUMAN 0.601 ELPEHTVK_476.8_347.2 VTDB_HUMAN 0.601 FTFTLHLETPKPSISSSNLNPR_829.4_874.4 PSG1_HUMAN 0.601 DLYHYITSYVVDGEIIIYGPAYSGR_955.5_707.3 PSG1_HUMAN 0.601 SPQAFYR_434.7_684.4 REL3_HUMAN 0.616 TPSAAYLWVGTGASEAEK_919.5_849.4 GELS_HUMAN 0.615 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 0.615 IEVNESGTVASSSTAVIVSAR_693.0_545.3 PAI1_HUMAN 0.615 LTTVDIVTLR_565.8_815.5 IL2RB_HUMAN 0.615 LWAYLTIQELLAK_781.5_371.2 ITIH1_HUMAN 0.613 SYTITGLQPGTDYK_772.4_352.2 FINC_HUMAN 0.612 GAVHVVVAETDYQSFAVLYLER_822.8_863.5 CO8G_HUMAN 0.612 FQLPGQK_409.2_276.1 PSG1_HUMAN 0.612 DLYHYITSYVVDGEIIIYGPAYSGR_955.5_707.3 PSG1_HUMAN 0.601

TABLE 16 Lasso Early 32 Variable Protein Coefficient LIQDAVTGLTVNGQITGDK_972.0_798.4 ITIH3_HUMAN 9.53 VQTAHFK_277.5_431.2 CO8A_HUMAN 9.09 FLNWIK_410.7_560.3 HABP2_HUMAN 6.15 ITGFLKPGK_320.9_429.3 LBP_HUMAN 5.29 ELIEELVNITQNQK_557.6_517.3 IL13_HUMAN 3.83 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 3.41 DISEVVTPR_508.3_787.4 CFAB_HUMAN 0.44 AHYDLR_387.7_288.2 FETUA_HUMAN 0.1

TABLE 17 Lasso Early 100 Variable Protein Coefficient LIQDAVTGLTVNGQITGDK_972.0_798.4 ITIH3_HUMAN 6.56 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 6.51 VQTAHFK_277.5_431.2 CO8A_HUMAN 4.51 NIQSVNVK_451.3_674.4 GROA_HUMAN 3.12 TYLHTYESEI_628.3_908.4 ENPP2_HUMAN 2.68 LIENGYFHPVK_439.6_627.4 F13B_HUMAN 2.56 AVLHIGEK_289.5_292.2 THBG_HUMAN 2.11 FLNWIK_410.7_560.3 HABP2_HUMAN 1.85 ITGFLKPGK_320.9_429.3 LBP_HUMAN 1.36 DALSSVQESQVAQQAR_573.0_672.4 APOC3_HUMAN 1.3 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN 0.83 FLPCENK_454.2_550.2 IL10_HUMAN 0.39 ELIEELVNITQNQK_557.6_517.3 IL13_HUMAN 0.3 TEFLSNYLTNVDDITLVPGTLGR_846.8_600.3 ENPP2_HUMAN 0.29 VSEADSSNADWVTK_754.9_347.2 CFAB_HUMAN 0.27 ITLPDFTGDLR_624.3_288.2 LBP_HUMAN 0.13 TGVAVNKPAEFTVDAK_549.6_258.1 FLNA_HUMAN 0.04 TASDFITK_441.7_781.4 GELS_HUMAN −5.91 LIQDAVTGLTVNGQITGDK_972.0_798.4 ITIH3_HUMAN 6.56

TABLE 18 Lasso Protein Early Window Variable Protein Coefficient ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 7.17 LIQDAVTGLTVNGQITGDK_972.0_798.4 ITIH3_HUMAN 6.06 LIENGYFHPVK_439.6_627.4 F13B_HUMAN 3.23 WWGGQPLWITATK_772.4_929.5 ENPP2_HUMAN 2.8 QALEEFQK_496.8_680.3 CO8B_HUMAN 2.73 NIQSVNVK_451.3_674.4 GROA_HUMAN 2.53 DALSSVQESQVAQQAR_573.0_672.4 APOC3_HUMAN 2.51 AVLHIGEK_289.5_348.7 THBG_HUMAN 2.33 FLNWIK_410.7_560.3 HABP2_HUMAN 1.05 FLPCENK_454.2_550.2 IL10_HUMAN 0.74 ITLPDFTGDLR_624.3_288.2 LBP_HUMAN 0.7 DISEVVTPR_508.3_787.4 CFAB_HUMAN 0.45 EVFSKPISWEELLQ_852.9_260.2 FA40A_HUMAN 0.17 YYGYTGAFR_549.3_450.3 TRFL_HUMAN 0.06 TASDFITK_441.7_781.4 GELS_HUMAN −7.65

TABLE 19 Lasso All Early Window Variable Protein Coefficient FLNWIK_410.7_560.3 HABP2_HUMAN 3.74 AHYDLR_387.7_288.2 FETUA_HUMAN 0.07 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 6.07 LIQDAVTGLTVNGQITGDK_972.0_798.4 ITIH3_HUMAN 8.85 TYLHTYESEI_628.3_908.4 ENPP2_HUMAN 2.97 VQTAHFK_277.5_431.2 CO8A_HUMAN 3.36 ELIEELVNITQNQK_557.6_618.3 IL13_HUMAN 11.24 VSEADSSNADWVTK_754.9_347.2 CFAB_HUMAN 0.63 AVLHIGEK_289.5_292.2 THBG_HUMAN 0.51 TGVAVNKPAEFTVDAK_549.6_977.5 FLNA_HUMAN 0.17 LIENGYFHPVK_439.6_343.2 F13B_HUMAN 1.7 AQPVQVAEGSEPDGFWEALGGK_758.0_574.3 GELS_HUMAN −0.93 YYGYTGAFR_549.3_450.3 TRFL_HUMAN 1.4 TASDFITK_441.7_781.4 GELS_HUMAN −0.07 NIQSVNVK_451.3_674.4 GROA_HUMAN 2.12 DALSSVQESQVAQQAR_573.0_672.4 APOC3_HUMAN 1.15 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN 0.09 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 2.45 ALDLSLK_380.2_575.3 ITIH3_HUMAN 2.51 TLFIFGVTK_513.3_811.5 PSG4_HUMAN 4.12 ISQGEADINIAFYQR_575.6_684.4 MMP8_HUMAN 1.29 SGVDLADSNQK_567.3_591.3 VGFR3_HUMAN 0.55 GPGEDFR_389.2_322.2 PTGDS_HUMAN 0.07 DPNGLPPEAQK_583.3_669.4 RET4_HUMAN 1.36 WNFAYWAAHQPWSR_607.3_545.3 PRG2_HUMAN −1.27 ELCLDPK_437.7_359.2 IL8_HUMAN 0.3 FFQYDTWK_567.8_840.4 IGF2_HUMAN 1.83 IIEVEEEQEDPYLNDR_996.0_777.4 FBLN1_HUMAN 1.14 ECEELEEK_533.2_405.2 IL15_HUMAN 1.78 LEEHYELR_363.5_580.3 PAI2_HUMAN 0.15 LNIGYIEDLK_589.3_837.4 PAI2_HUMAN 0.32 TAVTANLDIR_537.3_288.2 CHL1_HUMAN −0.98 SWNEPLYHLVTEVR_581.6_716.4 PRL_HUMAN 1.88 ILNIFGVIK_508.8_790.5 TFR1_HUMAN 0.05 TPSAAYLWVGTGASEAEK_919.5_849.4 GELS_HUMAN −2.69 VGVISFAQK_474.8_693.4 TFR2_HUMAN −5.68 LNIGYIEDLK_589.3_950.5 PAI2_HUMAN −1.43 GQVPENEANVVITTLK_571.3_462.3 CADH1_HUMAN −0.55 STPSLTTK_417.7_549.3 IL6RA_HUMAN −0.59 ALLLGWVPTR_563.3_373.2 PAR4_HUMAN −0.97

TABLE 20 Lasso SummedCoef Early Window Transition Protein SumBestCoefs LIQDAVTGLTVNGQITGDK_972.0_798.4 ITIH3_HUMAN 1173.723955 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 811.0150364 ELIEELVNITQNQK_557.6_618.3 IL13_HUMAN 621.9659363 VQTAHFK_277.5_431.2 CO8A_HUMAN 454.178544 NIQSVNVK_451.3_674.4 GROA_HUMAN 355.9550674 TLFIFGVTK_513.3_811.5 PSG4_HUMAN 331.8629189 GPGEDFR_389.2_322.2 PTGDS_HUMAN 305.9079494 FLPCENK_454.2_550.2 IL10_HUMAN 296.9473975 FLNWIK_410.7_560.3 HABP2_HUMAN 282.9841332 LIENGYFHPVK_439.6_627.4 F13B_HUMAN 237.5320227 ECEELEEK_533.2_405.2 IL15_HUMAN 200.38281 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 194.6252869 QALEEFQK_496.8_680.3 CO8B_HUMAN 179.2518843 IIEVEEEQEDPYLNDR_996.0_777.4 FBLN1_HUMAN 177.7534111 TYLHTYESEI_628.3_908.4 ENPP2_HUMAN 164.9735228 ELIEELVNITQNQK_557.6_517.3 IL13_HUMAN 162.2414693 LEEHYELR_363.5_580.3 PAI2_HUMAN 152.9262386 ISQGEADINIAFYQR_575.6_684.4 MMP8_HUMAN 144.2445011 HPWIVHWDQLPQYQLNR_744.0_918.5 KS6A3_HUMAN 140.2287926 AHYDLR_387.7_288.2 FETUA_HUMAN 137.9737525 GFQALGDAADIR_617.3_288.2 TIMP1_HUMAN 130.4945567 SWNEPLYHLVTEVR_581.6_716.4 PRL_HUMAN 127.442646 SGVDLADSNQK_567.3_591.3 VGFR3_HUMAN 120.5149446 YENYTSSFFIR_713.8_293.1 IL12B_HUMAN 117.0947487 FFQYDTWK_567.8_840.4 IGF2_HUMAN 109.8569617 HYFIAAVER_553.3_658.4 FA8_HUMAN 106.9426543 ITGFLKPGK_320.9_429.3 LBP_HUMAN 103.8056505 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN 98.50490812 SGVDLADSNQK_567.3_662.3 VGFR3_HUMAN 97.19989285 ALDLSLK_380.2_575.3 ITIH3_HUMAN 94.84900337 TGVAVNKPAEFTVDAK_549.6_258.1 FLNA_HUMAN 92.52335783 HPWIVHWDQLPQYQLNR_744.0_1047.0 KS6A3_HUMAN 91.77547608 LIQDAVTGLTVNGQITGDK_972.0_640.4 ITIH3_HUMAN 83.6483639 LNIGYIEDLK_589.3_837.4 PAI2_HUMAN 83.50221521 IALGGLLFPASNLR_481.3_657.4 SHBG_HUMAN 79.33146741 LPATEKPVLLSK_432.6_460.3 HYOU1_HUMAN 78.89429168 FQLSETNR_497.8_605.3 PSG2_HUMAN 78.13445824 NEIVFPAGILQAPFYTR_968.5_357.2 ECE1_HUMAN 75.12145257 ALDLSLK_380.2_185.1 ITIH3_HUMAN 63.05454715 DLHLSDVFLK_396.2_366.2 CO6_HUMAN 58.26831142 TQILEWAAER_608.8_761.4 EGLN_HUMAN 57.29461621 FSVVYAK_407.2_381.2 FETUA_HUMAN 54.78436389 VSEADSSNADWVTK_754.9_347.2 CFAB_HUMAN 54.40003244 DPNGLPPEAQK_583.3_669.4 RET4_HUMAN 53.89169348 VQEAHLTEDQIFYFPK_655.7_701.4 CO8G_HUMAN 53.33747599 LSSPAVITDK_515.8_830.5 PLMN_HUMAN 53.22513181 ITLPDFTGDLR_624.3_288.2 LBP_HUMAN 51.5477235 AVLHIGEK_289.5_292.2 THBG_HUMAN 49.73092632 GEVTYTTSQVSK_650.3_750.4 EGLN_HUMAN 45.14743629 GYVIIKPLVWV_643.9_854.6 SAMP_HUMAN 44.05164273 TGVAVNKPAEFTVDAK_549.6_977.5 FLNA_HUMAN 42.99898046 YYGYTGAFR_549.3_450.3 TRFL_HUMAN 42.90897411 ILDGGNK_358.7_490.2 CXCL5_HUMAN 42.60771281 FLPCENK_454.2_390.2 IL10_HUMAN 42.56799651 GFQALGDAADIR_617.3_717.4 TIMP1_HUMAN 38.68456017 SDGAKPGPR_442.7_213.6 COLI_HUMAN 38.47800265 NTGVISVVTTGLDR_716.4_662.4 CADH1_HUMAN 32.62953675 SERPPIFEIR_415.2_288.2 LRP1_HUMAN 31.48248968 DFHINLFQVLPWLK_885.5_400.2 CFAB_HUMAN 31.27286268 DALSSVQESQVAQQAR_573.0_672.4 APOC3_HUMAN 31.26972354 ELCLDPK_437.7_359.2 IL8_HUMAN 29.91108737 ILNIFGVIK_508.8_790.5 TFR1_HUMAN 29.88784921 TEFLSNYLTNVDDITLVPGTLGR_846.8_600.3 ENPP2_HUMAN 29.42327998 GAVHVVVAETDYQSFAVLYLER_822.8_863.5 CO8G_HUMAN 26.70286929 AVLHIGEK_289.5_348.7 THBG_HUMAN 25.78703299 TFLTVYWTPER_706.9_401.2 ICAM1_HUMAN 24.73090242 AGITIPR_364.2_486.3 IL17_HUMAN 23.84580477 GAVHVVVAETDYQSFAVLYLER_822.8_580.3 CO8G_HUMAN 23.81167843 SLQAFVAVAAR_566.8_487.3 IL23A_HUMAN 23.61468839 SWNEPLYHLVTEVR_581.6_614.3 PRL_HUMAN 23.2538221 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 22.70115313 TAHISGLPPSTDFIVYLSGLAPSIR_871.5_800.5 TENA_HUMAN 22.42695892 QNYHQDSEAAINR_515.9_544.3 FRIH_HUMAN 21.96827269 AHQLAIDTYQEFEETYIPK_766.0_634.4 CSH_HUMAN 21.75765717 GDTYPAELYITGSILR_885.0_274.1 F13B_HUMAN 20.89751398 AHYDLR_387.7_566.3 FETUA_HUMAN 20.67629529 IALGGLLFPASNLR_481.3_412.3 SHBG_HUMAN 19.28973033 ATNATLDPR_479.8_272.2 PAR1_HUMAN 18.77604574 FSVVYAK_407.2_579.4 FETUA_HUMAN 17.81136564 HTLNQIDEVK_598.8_951.5 FETUA_HUMAN 17.29763288 DIPHWLNPTR_416.9_373.2 PAPP1_HUMAN 17.00562521 LYYGDDEK_501.7_563.2 CO8A_HUMAN 16.78897272 AALAAFNAQNNGSNFQLEEISR_789.1_633.3 FETUA_HUMAN 16.41986569 IQTHSTTYR_369.5_627.3 F13B_HUMAN 15.78335174 GPITSAAELNDPQSILLR_632.4_826.5 EGLN_HUMAN 15.3936876 QTLSWTVTPK_580.8_818.4 PZP_HUMAN 14.92509259 AVGYLITGYQR_620.8_737.4 PZP_HUMAN 13.9795325 DIIKPDPPK_511.8_342.2 IL12B_HUMAN 13.76508282 YNQLLR_403.7_288.2 ENOA_HUMAN 12.61733711 GNGLTWAEK_488.3_634.3 C163B_HUMAN 12.5891421 QVFAVQR_424.2_473.3 ELNE_HUMAN 12.57709327 FLQEQGHR_338.8_497.3 CO8G_HUMAN 12.51843475 HVVQLR_376.2_515.3 IL6RA_HUMAN 11.83747559 DVLLLVHNLPQNLTGHIWYK_791.8_883.0 PSG7_HUMAN 11.69074708 TFLTVYWTPER_706.9_502.3 ICAM1_HUMAN 11.63709776 VELAPLPSWQPVGK_760.9_400.3 ICAM1_HUMAN 10.79897269 TLFIFGVTK_513.3_215.1 PSG4_HUMAN 10.2831751 AYSDLSR_406.2_375.2 SAMP_HUMAN 10.00461148 HATLSLSIPR_365.6_472.3 VGFR3_HUMAN 9.967933028 LQGTLPVEAR_542.3_571.3 CO5_HUMAN 9.963760572 NTVISVNPSTK_580.3_732.4 VCAM1_HUMAN 9.124228658 EVFSKPISWEELLQ_852.9_260.2 FA40A_HUMAN 8.527980294 SLQNASAIESILK_687.4_860.5 IL3_HUMAN 8.429061621 IQHPFTVEEFVLPK_562.0_861.5 PZP_HUMAN 7.996504258 GVTGYFTFNLYLK_508.3_683.9 PSG5_HUMAN 7.94396229 VFQYIDLHQDEFVQTLK_708.4_361.2 CNDP1_HUMAN 7.860590049 ILDDLSPR_464.8_587.3 ITIH4_HUMAN 7.593889262 LIENGYFHPVK_439.6_343.2 F13B_HUMAN 7.05838337 VFQFLEK_455.8_811.4 CO5_HUMAN 6.976884759 AFTECCVVASQLR_770.9_574.3 CO5_HUMAN 6.847474286 WWGGQPLWITATK_772.4_929.5 ENPP2_HUMAN 6.744837357 IQTHSTTYR_369.5_540.3 F13B_HUMAN 6.71464509 IAQYYYTFK_598.8_395.2 F13B_HUMAN 6.540497911 YGFYTHVFR_397.2_421.3 THRB_HUMAN 6.326347548 YHFEALADTGISSEFYDNANDLLSK_940.8_874.5 CO8A_HUMAN 6.261787525 ANDQYLTAAALHNLDEAVK_686.4_301.1 IL1A_HUMAN 6.217191651 FSLVSGWGQLLDR_493.3_403.2 FA7_HUMAN 6.1038295 GWVTDGFSSLK_598.8_854.4 APOC3_HUMAN 6.053494609 TLEAQLTPR_514.8_814.4 HEP2_HUMAN 5.855967278 VSAPSGTGHLPGLNPL_506.3_300.7 PSG3_HUMAN 5.625944609 EAQLPVIENK_570.8_699.4 PLMN_HUMAN 5.407703773 SPEAEDPLGVER_649.8_670.4 Z512B_HUMAN 5.341420139 IAIDLFK_410.3_635.4 HEP2_HUMAN 4.698739039 YEFLNGR_449.7_293.1 PLMN_HUMAN 4.658286706 VQTAHFK_277.5_502.3 CO8A_HUMAN 4.628247194 IEVIITLK_464.8_815.5 CXL11_HUMAN 4.57198762 ILTPEVR_414.3_601.3 GDF15_HUMAN 4.452884608 LEEHYELR_363.5_288.2 PAI2_HUMAN 4.411983862 HATLSLSIPR_365.6_272.2 VGFR3_HUMAN 4.334242077 NSDQEIDFK_548.3_294.2 S10A5_HUMAN 4.25302369 LPNNVLQEK_527.8_844.5 AFAM_HUMAN 4.183602548 ELANTIK_394.7_475.3 S10AC_HUMAN 4.13558153 LSIPQITTK_500.8_687.4 PSG5_HUMAN 3.966238797 TLNAYDHR_330.5_312.2 PAR3_HUMAN 3.961140111 WWGGQPLWITATK_772.4_373.2 ENPP2_HUMAN 3.941476057 ELLESYIDGR_597.8_710.4 THRB_HUMAN 3.832723338 ATLSAAPSNPR_542.8_570.3 CXCL2_HUMAN 3.82834767 VVLSSGSGPGLDLPLVLGLPLQLK_791.5_598.4 SHBG_HUMAN 3.80737887 NADYSYSVWK_616.8_333.2 CO5_HUMAN 3.56404167 ILILPSVTR_506.3_559.3 PSGx_HUMAN 3.526998593 ALEQDLPVNIK_620.4_798.5 CNDP1_HUMAN 3.410412424 QVCADPSEEWVQK_788.4_275.2 CCL3_HUMAN 3.30795151 SVQNDSQAIAEVLNQLK_619.7_914.5 DESP_HUMAN 3.259270741 QVFAVQR_424.2_620.4 ELNE_HUMAN 3.211482663 ALPGEQQPLHALTR_511.0_807.5 IBP1_HUMAN 3.211207158 LEPLYSASGPGLRPLVIK_637.4_260.2 CAA60698 3.203088951 GTYLYNDCPGPGQDTDCR_697.0_666.3 TNR1A_HUMAN 3.139418139 DAGLSWGSAR_510.2_576.3 NEUR4_HUMAN 3.005197927 YGFYTHVFR_397.2_659.4 THRB_HUMAN 2.985663918 NNQLVAGYLQGPNVNLEEK_700.7_357.2 IL1RA_HUMAN 2.866983196 EKPAGGIPVLGSLVNTVLK_631.4_930.6 BPIB1_HUMAN 2.798965142 FGSDDEGR_441.7_735.3 PTHR_HUMAN 2.743283546 IEVNESGTVASSSTAVIVSAR_693.0_545.3 PAI1_HUMAN 2.699725572 FATTFYQHLADSK_510.3_533.3 ANT3_HUMAN 2.615073729 DYWSTVK_449.7_347.2 APOC3_HUMAN 2.525459346 QLGLPGPPDVPDHAAYHPF_676.7_263.1 ITIH4_HUMAN 2.525383799 LSSPAVITDK_515.8_743.4 PLMN_HUMAN 2.522306831 TEFLSNYLTNVDDITLVPGTLGR_846.8_699.4 ENPP2_HUMAN 2.473366805 SILFLGK_389.2_201.1 THBG_HUMAN 2.472413913 VTFEYR_407.7_614.3 CRHBP_HUMAN 2.425338167 SVVLIPLGAVDDGEHSQNEK_703.0_798.4 CNDP1_HUMAN 2.421340244 HTLNQIDEVK_598.8_958.5 FETUA_HUMAN 2.419851187 ALNSIIDVYHK_424.9_661.3 S10A8_HUMAN 2.367904596 ETLALLSTHR_570.8_500.3 IL5_HUMAN 2.230076769 GLQYAAQEGLLALQSELLR_1037.1_858.5 LBP_HUMAN 2.205949216 TYNVDK_370.2_262.1 PPB1_HUMAN 2.11849772 FTITAGSK_412.7_576.3 FABPL_HUMAN 2.098589805 GIVEECCFR_585.3_900.3 IGF2_HUMAN 2.059942995 YGIEEHGK_311.5_599.3 CXA1_HUMAN 2.033828589 ALVLELAK_428.8_331.2 INHBE_HUMAN 1.993820617 ITLPDFTGDLR_624.3_920.5 LBP_HUMAN 1.968753183 HELTDEELQSLFTNFANVVDK_817.1_906.5 AFAM_HUMAN 1.916438806 EANQSTLENFLER_775.9_678.4 IL4_HUMAN 1.902033355 DADPDTFFAK_563.8_825.4 AFAM_HUMAN 1.882254674 LFIPQITR_494.3_727.4 PSG9_HUMAN 1.860649392 DPNGLPPEAQK_583.3_497.2 RET4_HUMAN 1.847702127 VEPLYELVTATDFAYSSTVR_754.4_549.3 CO8B_HUMAN 1.842159131 FQLSETNR_497.8_476.3 PSG2_HUMAN 1.834693717 FSLVSGWGQLLDR_493.3_516.3 FA7_HUMAN 1.790582748 NKPGVYTDVAYYLAWIR_677.0_545.3 FA12_HUMAN 1.777303353 FTGSQPFGQGVEHATANK_626.0_521.2 TSP1_HUMAN 1.736517431 DDLYVSDAFHK_655.3_704.3 ANT3_HUMAN 1.717534082 AFLEVNEEGSEAAASTAVVIAGR_764.4_685.4 ANT3_HUMAN 1.679420475 LPNNVLQEK_527.8_730.4 AFAM_HUMAN 1.66321148 IVLSLDVPIGLLQILLEQAR_735.1_503.3 UCN2_HUMAN 1.644983604 DPTFIPAPIQAK_433.2_556.3 ANGT_HUMAN 1.625411496 SDLEVAHYK_531.3_617.3 CO8B_HUMAN 1.543640117 QLYGDTGVLGR_589.8_501.3 CO8G_HUMAN 1.505242962 VNHVTLSQPK_374.9_459.3 B2MG_HUMAN 1.48233058 TLLPVSKPEIR_418.3_288.2 CO5_HUMAN 1.439531341 SEYGAALAWEK_612.8_845.5 CO6_HUMAN 1.424401638 YGIEEHGK_311.5_341.2 CXA1_HUMAN 1.379872204 DAGLSWGSAR_510.3_390.2 NEUR4_HUMAN 1.334272677 AEHPTWGDEQLFQTTR_639.3_569.3 PGH1_HUMAN 1.30549273 FQSVFTVTR_542.8_623.4 C1QC_HUMAN 1.302847429 VPGLYYFTYHASSR_554.3_420.2 C1QB_HUMAN 1.245565877 AYSDLSR_406.2_577.3 SAMP_HUMAN 1.220777002 ALEQDLPVNIK_620.4_570.4 CNDP1_HUMAN 1.216612522 NAVVQGLEQPHGLVVHPLR_688.4_890.6 LRP1_HUMAN 1.212935735 TSDQIHFFFAK_447.6_659.4 ANT3_HUMAN 1.176238265 GTYLYNDCPGPGQDTDCR_697.0_335.2 TNR1A_HUMAN 1.1455649 TSYQVYSK_488.2_787.4 C163A_HUMAN 1.048896429 ALNSIIDVYHK_424.9_774.4 S10A8_HUMAN 1.028522516 VELAPLPSWQPVGK_760.9_342.2 ICAM1_HUMAN 0.995831393 LSETNR_360.2_330.2 PSG1_HUMAN 0.976094717 HFQNLGK_422.2_527.2 AFAM_HUMAN 0.956286531 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN 0.947931674 LPATEKPVLLSK_432.6_347.2 HYOU1_HUMAN 0.932537153 SPEAEDPLGVER_649.8_314.1 Z512B_HUMAN 0.905955419 DEIPHNDIALLK_459.9_510.8 HABP2_HUMAN 0.9032484 FFQYDTWK_567.8_712.3 IGF2_HUMAN 0.884340285 LIEIANHVDK_384.6_498.3 ADA12_HUMAN 0.881493383 AGFAGDDAPR_488.7_701.3 ACTB_HUMAN 0.814836556 YEFLNGR_449.7_606.3 PLMN_HUMAN 0.767373087 VIAVNEVGR_478.8_284.2 CHL1_HUMAN 0.721519592 SLSQQIENIR_594.3_531.3 CO1A1_HUMAN 0.712051082 EWVAIESDSVQPVPR_856.4_486.2 CNDP1_HUMAN 0.647712421 YGLVTYATYPK_638.3_843.4 CFAB_HUMAN 0.618499569 SVVLIPLGAVDDGEHSQNEK_703.0_286.2 CNDP1_HUMAN 0.606626346 NSDQEIDFK_548.3_409.2 S10A5_HUMAN 0.601928175 NVNQSLLELHK_432.2_543.3 FRIH_HUMAN 0.572008792 IAQYYYTFK_598.8_884.4 F13B_HUMAN 0.495062844 GPITSAAELNDPQSILLR_632.4_601.4 EGLN_HUMAN 0.47565795 YTTEIIK_434.2_704.4 C1R_HUMAN 0.433318952 GYVIIKPLVWV_643.9_304.2 SAMP_HUMAN 0.427905264 LDFHFSSDR_375.2_464.2 INHBC_HUMAN 0.411898116 IPSNPSHR_303.2_496.3 FBLN3_HUMAN 0.390037291 APLTKPLK_289.9_357.2 CRP_HUMAN 0.38859469 EVFSKPISWEELLQ_852.9_376.2 FA40A_HUMAN 0.371359974 YENYTSSFFIR_713.8_756.4 IL12B_HUMAN 0.346336267 SPQAFYR_434.7_556.3 REL3_HUMAN 0.345901234 SVDEALR_395.2_488.3 PRDX2_HUMAN 0.307518869 FVFGTTPEDILR_697.9_742.4 TSP1_HUMAN 0.302313589 FTFTLHLETPKPSISSSNLNPR_829.4_787.4 PSG1_HUMAN 0.269826678 VGEYSLYIGR_578.8_708.4 SAMP_HUMAN 0.226573173 ILPSVPK_377.2_244.2 PGH1_HUMAN 0.225429414 LFIPQITR_494.3_614.4 PSG9_HUMAN 0.18285533 TGYYFDGISR_589.8_857.4 FBLN1_HUMAN 0.182474114 HYGGLTGLNK_530.3_759.4 PGAM1_HUMAN 0.152397007 NQSPVLEPVGR_598.3_866.5 KS6A3_HUMAN 0.128963949 IGKPAPDFK_324.9_294.2 PRDX2_HUMAN 0.113383235 TSESTGSLPSPFLR_739.9_716.4 PSMG1_HUMAN 0.108159874 ESDTSYVSLK_564.8_347.2 CRP_HUMAN 0.08569303 ETPEGAEAKPWYEPIYLGGVFQLEK_951.1_877.5 TNFA_HUMAN 0.039781728 TSDQIHFFFAK_447.6_512.3 ANT3_HUMAN 0.008064465

TABLE 21 Lasso32 Middle Window Co- effi- Variable UniProt_ID cient SEYGAALAWEK_612.8_788.4 CO6_HUMAN 6.99 VFQFLEK_455.8_811.4 CO5_HUMAN 6.43 VLEPTLK_400.3_458.3 VTDB_HUMAN 3.99 SLDFTELDVAAEK_719.4_316.2 ANGT_HUMAN 3.33 TLAFVR_353.7_492.3 FA7_HUMAN 2.44 YGIEEHGK_311.5_599.3 CXA1_HUMAN 2.27 LHEAFSPVSYQHDLALLR_699.4_251.2 FA12_HUMAN 2.14 QGHNSVFLIK_381.6_520.4 HEMO_HUMAN 0.25 LLAPSDSPEWLSFDVTGVVR_730.1_430.3 TGFB1_HUMAN −2.81 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN −3.46 VNHVTLSQPK_374.9_244.2 B2MG_HUMAN −6.61

TABLE 22 Lasso100 Middle Window Co- effi- Variable UniProt_ID cient VFQFLEK_455.8_811.4 CO5_HUMAN 6.89 SEYGAALAWEK_612.8_788.4 CO6_HUMAN 4.67 GEVTYTTSQVSK_650.3_750.4 EGLN_HUMAN 3.4 QVFAVQR_424.2_473.3 ELNE_HUMAN 1.94 VELAPLPSWQPVGK_760.9_342.2 ICAM1_HUMAN 1.91 LHEAFSPVSYQHDLALLR_699.4_251.2 FA12_HUMAN 1.8 SLDFTELDVAAEK_719.4_316.2 ANGT_HUMAN 1.67 YGIEEHGK_311.5_599.3 CXA1_HUMAN 1.53 YGIEEHGK_311.5_341.2 CXA1_HUMAN 1.51 HYINLITR_515.3_301.1 NPY_HUMAN 1.47 TLAFVR_353.7_492.3 FA7_HUMAN 1.46 GVTGYFTFNLYLK_508.3_260.2 PSG5_HUMAN 1.28 FSLVSGWGQLLDR_493.3_403.2 FA7_HUMAN 0.84 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN 0.41 VELAPLPSWQPVGK_760.9_400.3 ICAM1_HUMAN 0.3 AVDIPGLEAATPYR_736.9_399.2 TENA_HUMAN −0.95 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN −1.54 DVLLLVHNLPQNLTGHIWYK_791.8_310.2 PSG7_HUMAN −1.54 VPLALFALNR_557.3_620.4 PEPD_HUMAN −1.91 LLAPSDSPEWLSFDVTGVVR_730.1_430.3 TGFB1_HUMAN −2.3 VNHVTLSQPK_374.9_244.2 B2MG_HUMAN −3.6 EVFSKPISWEELLQ_852.9_376.2 FA40A_HUMAN −3.96

TABLE 23 Lasso Protein Middle Window Co- effi- Variable UniProt_ID cient SEYGAALAWEK_612.8_788.4 CO6_HUMAN 5.84 VFQFLEK_455.8_811.4 CO5_HUMAN 5.58 SLDFTELDVAAEK_719.4_316.2 ANGT_HUMAN 2.11 TLAFVR_353.7_492.3 FA7_HUMAN 1.83 LHEAFSPVSYQHDLALLR_699.4_251.2 FA12_HUMAN 1.62 HYINLITR_515.3_301.1 NPY_HUMAN 1.39 VLEPTLK_400.3_458.3 VTDB_HUMAN 1.37 YGIEEHGK_311.5_599.3 CXA1_HUMAN 1.17 VELAPLPSWQPVGK_760.9_342.2 ICAM1_HUMAN 1.13 QVFAVQR_424.2_473.3 ELNE_HUMAN 0.79 ANLINNIFELAGLGK_793.9_299.2 LCAP_HUMAN 0.23 DVLLLVHNLPQNLTGHIWYK_791.8_310.2 PSG7_HUMAN −0.61 VEHSDLSFSK_383.5_234.1 B2MG_HUMAN −0.69 AVDIPGLEAATPYR_736.9_399.2 TENA_HUMAN −0.85 VPLALFALNR_557.3_620.4 PEPD_HUMAN −1.45 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN −1.9 LLAPSDSPEWLSFDVTGVVR_730.1_430.3 TGFB1_HUMAN −2.07 EVFSKPISWEELLQ_852.9_376.2 FA40A_HUMAN −2.32

TABLE 24 Lasso All Middle Window Co- effi- Variable UniProt_ID cient SEYGAALAWEK_612.8_788.4 CO6_HUMAN 2.48 VFQFLEK_455.8_811.4 CO5_HUMAN 2.41 SLDFTELDVAAEK_719.4_316.2 ANGT_HUMAN 1.07 YGIEEHGK_311.5_599.3 CXA1_HUMAN 0.64 VLEPTLK_400.3_458.3 VTDB_HUMAN 0.58 LHEAFSPVSYQHDLALLR_699.4_251.2 FA12_HUMAN 0.21 LLAPSDSPEWLSFDVTGVVR_730.1_430.3 TGFB1_HUMAN −0.62 VNHVTLSQPK_374.9_244.2 B2MG_HUMAN −1.28

TABLE 25 Lasso32 Middle-Late Window Variable UniProt_ID Coefficient SEYGAALAWEK_612.8_845.5 CO6_HUMAN 4.35 TLAFVR_353.7_492.3 FA7_HUMAN 2.42 YGIEEHGK_311.5_599.3 CXA1_HUMAN 1.46 DFNQFSSGEK_386.8_333.2 FETA_HUMAN 1.37 VFQFLEK_455.8_811.4 CO5_HUMAN 0.89 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 0.85 QINSYVK_426.2_496.3 CBG_HUMAN 0.56 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 0.53 SLQAFVAVAAR_566.8_804.5 IL23A_HUMAN 0.39 TEQAAVAR_423.2_615.4 FA12_HUMAN 0.26 VLEPTLK_400.3_587.3 VTDB_HUMAN 0.24 AQPVQVAEGSEPDGFWEALGGK_758.0_574.3 GELS_HUMAN −2.08 VPLALFALNR_557.3_620.4 PEPD_HUMAN −2.09 AVYEAVLR_460.8_587.4 PEPD_HUMAN −3.37

TABLE 26 Lasso100 Middle-Late Window Variable UniProt_ID Coefficient VFQFLEK_455.8_811.4 CO5_HUMAN 3.82 SEYGAALAWEK_612.8_845.5 CO6_HUMAN 2.94 YGIEEHGK_311.5_599.3 CXA1_HUMAN 2.39 DPTFIPAPIQAK_433.2_556.3 ANGT_HUMAN 2.05 TLAFVR_353.7_492.3 FA7_HUMAN 1.9 NQSPVLEPVGR_598.3_866.5 KS6A3_HUMAN 1.87 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 1.4 TQILEWAAER_608.8_761.4 EGLN_HUMAN 1.29 VVGGLVALR_442.3_784.5 FA12_HUMAN 1.24 QINSYVK_426.2_496.3 CBG_HUMAN 1.14 YGIEEHGK_311.5_341.2 CXA1_HUMAN 0.84 ALEQDLPVNIK_620.4_570.4 CNDP1_HUMAN 0.74 GTYLYNDCPGPGQDTDCR_697.0_666.3 TNR1A_HUMAN 0.51 SLQNASAIESILK_687.4_860.5 IL3_HUMAN 0.44 DLHLSDVFLK_396.2_260.2 CO6_HUMAN 0.38 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 0.37 NIQSVNVK_451.3_674.4 GROA_HUMAN 0.3 FFQYDTWK_567.8_712.3 IGF2_HUMAN 0.19 ANLINNIFELAGLGK_793.9_299.2 LCAP_HUMAN 0.19 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 0.15 AALAAFNAQNNGSNFQLEEISR_789.1_746.4 FETUA_HUMAN −0.09 AQPVQVAEGSEPDGFWEALGGK_758.0_574.3 GELS_HUMAN −0.52 TSYQVYSK_488.2_787.4 C163A_HUMAN −0.62 AVDIPGLEAATPYR_736.9_399.2 TENA_HUMAN −1.29 TAHISGLPPSTDFIVYLSGLAPSIR_871.5_472.3 TENA_HUMAN −1.53 AEIEYLEK_497.8_552.3 LYAM1_HUMAN −1.73 LLAPSDSPEWLSFDVTGVVR_730.1_430.3 TGFB1_HUMAN −1.95 VPLALFALNR_557.3_620.4 PEPD_HUMAN −2.9 AVYEAVLR_460.8_587.4 PEPD_HUMAN −3.04 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN −3.49 EVFSKPISWEELLQ_852.9_376.2 FA40A_HUMAN −3.71

TABLE 27 Lasso Protein Middle-LateWindow Variable UniProt_ID Coefficient VFQFLEK_455.8_811.4 CO5_HUMAN 4.25 ALNHLPLEYNSALYSR_621.0_696.4 CO6_HUMAN 3.06 YGIEEHGK_311.5_599.3 CXA1_HUMAN 2.36 SEPRPGVLLR_375.2_654.4 FA7_HUMAN 2.11 TQILEWAAER_608.8_761.4 EGLN_HUMAN 1.81 NQSPVLEPVGR_598.3_866.5 KS6A3_HUMAN 1.79 TEQAAVAR_423.2_615.4 FA12_HUMAN 1.72 QINSYVK_426.2_496.3 CBG_HUMAN 0.98 ALEQDLPVNIK_620.4_570.4 CNDP1_HUMAN 0.98 NCSFSIIYPVVIK_770.4_555.4 CRHBP_HUMAN 0.76 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 0.63 SLQNASAIESILK_687.4_860.5 IL3_HUMAN 0.59 ANLINNIFELAGLGK_793.9_299.2 LCAP_HUMAN 0.55 GTYLYNDCPGPGQDTDCR_697.0_666.3 TNR1A_HUMAN 0.55 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 0.46 NIQSVNVK_451.3_674.4 GROA_HUMAN 0.22 LTTVDIVTLR_565.8_815.5 IL2RB_HUMAN 0.11 FFQYDTWK_567.8_712.3 IGF2_HUMAN 0.01 TSYQVYSK_488.2_787.4 C163A_HUMAN −0.76 AQPVQVAEGSEPDGFWEALGGK_758.0_574.3 GELS_HUMAN −1.31 AEIEYLEK_497.8_552.3 LYAM1_HUMAN −1.59 LLAPSDSPEWLSFDVTGVVR_730.1_430.3 TGFB1_HUMAN −1.73 AVDIPGLEAATPYR_736.9_399.2 TENA_HUMAN −2.02 EVFSKPISWEELLQ_852.9_376.2 FA40A_HUMAN −3 TGVAVNKPAEFTVDAK_549.6_258.1 FLNA_HUMAN −3.15 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN −3.49 VNHVTLSQPK_374.9_244.2 B2MG_HUMAN −3.82 VPLALFALNR_557.3_620.4 PEPD_HUMAN −4.94

TABLE 28 Lasso All Middle-LateWindow Variable UniProt_ID Coefficient ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 2.38 TLAFVR_353.7_492.3 FA7_HUMAN 0.96 YGIEEHGK_311.5_599.3 CXA1_HUMAN 0.34 DPTFIPAPIQAK_433.2_461.2 ANGT_HUMAN 0.33 DFNQFSSGEK_386.8_333.2 FETA_HUMAN 0.13 QINSYVK_426.2_496.3 CBG_HUMAN 0.03 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 0 AQPVQVAEGSEPDGFWEALGGK_758.0_574.3 GELS_HUMAN −0.02 AEIEYLEK_497.8_552.3 LYAM1_HUMAN −0.05 VNHVTLSQPK_374.9_244.2 B2MG_HUMAN −0.12 LLAPSDSPEWLSFDVTGVVR_730.1_430.3 TGFB1_HUMAN −0.17 EVFSKPISWEELLQ_852.9_376.2 FA40A_HUMAN −0.31 AVDIPGLEAATPYR_736.9_399.2 TENA_HUMAN −0.35 VPLALFALNR_557.3_620.4 PEPD_HUMAN −0.43 AVYEAVLR_460.8_587.4 PEPD_HUMAN −2.33

TABLE 29 Lasso 32 LateWindow Variable  U niProt_ID  Coefficient QINSYVK_426.2_610.3 CBG_HUMAN 3.24 ILDGGNK_358.7_603.3 CXCL5_HUMAN 2.65 VFQYIDLHQDEFVQTLK_708.4_375.2 CNDP1_HUMAN 2.55 SGVDLADSNQK_567.3_662.3 VGFR3_HUMAN 2.12 YSHYNER_323.5_418.2 HABP2_HUMAN 1.63 DEIPHNDIALLK_459.9_510.8 HABP2_HUMAN 1.22 SGVDLADSNQK_567.3_591.3 VGFR3_HUMAN 0.96 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 0.86 GTYLYNDCPGPGQDTDCR_697.0_666.3 TNR1A_HUMAN 0.45 TSYQVYSK_488.2_787.4 C163A_HUMAN −1.73 TGVAVNKPAEFTVDAK_549.6_258.1 FLNA_HUMAN −2.56 SPEAEDPLGVER_649.8_314.1 Z512B_HUMAN −3.04 VPLALFALNR_557.3_620.4 PEPD_HUMAN −3.33 YYGYTGAFR_549.3_450.3 TRFL_HUMAN −4.24 AVYEAVLR_460.8_587.4 PEPD_HUMAN −5.83 AEIEYLEK_497.8_552.3 LYAM1_HUMAN −6.52 AALAAFNAQNNGSNFQLEEISR_789.1_746.4 FETUA_HUMAN −6.55

TABLE 30 Lasso 100 Late Window Variable UniProt_ID  Coefficient SGVDLADSNQK_567.3_662.3 VGFR3_HUMAN 4.13 ILDGGNK_358.7_603.3 CXCL5_HUMAN 3.57 QINSYVK_426.2_610.3 CBG_HUMAN 3.41 DEIPHNDIALLK_459.9_510.8 HABP2_HUMAN 1.64 VFQYIDLHQDEFVQTLK_708.4_375.2 CNDP1_HUMAN 1.57 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 1.45 LTTVDIVTLR_565.8_815.5 IL2RB_HUMAN 0.71 YSHYNER_323.5_418.2 HABP2_HUMAN 0.68 FFQYDTWK_567.8_712.3 IGF2_HUMAN 0.42 IEVNESGTVASSSTAVIVSAR_693.0_545.3 PAI1_HUMAN 0.36 GTYLYNDCPGPGQDTDCR_697.0_666.3 TNR1A_HUMAN 0.21 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 0.1 VGVISFAQK_474.8_580.3 TFR2_HUMAN 0.08 TSYQVYSK_488.2_787.4 C163A_HUMAN −0.36 ALNFGGIGVVVGHELTHAFDDQGR_837.1_360.2 ECE1_HUMAN −0.65 AYSDLSR_406.2_375.2 SAMP_HUMAN −1.23 TGVAVNKPAEFTVDAK_549.6_258.1 FLNA_HUMAN −1.63 SPEAEDPLGVER_649.8_314.1 Z512B_HUMAN −2.29 YYGYTGAFR_549.3_450.3 TRFL_HUMAN −2.58 VPLALFALNR_557.3_620.4 PEPD_HUMAN −2.73 YISPDQLADLYK_713.4_277.2 ENOA_HUMAN −2.87 AVDIPGLEAATPYR_736.9_286.1 TENA_HUMAN −3.9 AEIEYLEK_497.8_552.3 LYAM1_HUMAN −5.29 AVYEAVLR_460.8_587.4 PEPD_HUMAN −5.51 AALAAFNAQNNGSNFQLEEISR_789.1_746.4 FETUA_HUMAN −6.49

TABLE 31 Lasso Protein Late Window Variable UniProt_ID Coefficient SGVDLADSNQK_567.3_662.3 VGFR3_HUMAN 3.33 ILDGGNK_358.7_603.3 CXCL5_HUMAN 3.25 QINSYVK_426.2_496.3 CBG_HUMAN 2.41 YSHYNER_323.5_418.2 HABP2_HUMAN 1.82 ALEQDLPVNIK_620.4_798.5 CNDP1_HUMAN 1.32 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 1.27 GTYLYNDCPGPGQDTDCR_697.0_666.3 TNR1A_HUMAN 0.26 IEVNESGTVASSSTAVIVSAR_693.0_545.3 PAI1_HUMAN 0.18 LTTVDIVTLR_565.8_815.5 IL2RB_HUMAN 0.18 TSYQVYSK_488.2_787.4 C163A_HUMAN −0.11 TGVAVNKPAEFTVDAK_549.6_258.1 FLNA_HUMAN −0.89 AYSDLSR_406.2_375.2 SAMP_HUMAN −1.47 SPEAEDPLGVER_649.8_314.1 Z512B_HUMAN −1.79 YYGYTGAFR_549.3_450.3 TRFL_HUMAN −2.22 YISPDQLADLYK_713.4_277.2 ENOA_HUMAN −2.41 AVDIPGLEAATPYR_736.9_286.1 TENA_HUMAN −2.94 AEIEYLEK_497.8_552.3 LYAM1_HUMAN −5.18 AALAAFNAQNNGSNFQLEEISR_789.1_746.4 FETUA_HUMAN −5.71 AVYEAVLR_460.8_587.4 PEPD_HUMAN −7.33

TABLE 32 Lasso All Late Window Variable  U niProt_ID  Coefficient QINSYVK_426.2_496.3 CBG_HUMAN 0.5 DEIPHNDIALLK_459.9_510.8 HABP2_HUMAN 0.15 ALEQDLPVNIK_620.4_570.4 CNDP1_HUMAN 0.11 ILDGGNK_358.7_603.3 CXCL5_HUMAN 0.08 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 0.06 YYGYTGAFR_549.3_450.3 TRFL_HUMAN −0.39 AALAAFNAQNNGSNFQLEEISR_789.1_746.4 FETUA_HUMAN −1.57 AEIEYLEK_497.8_552.3 LYAM1_HUMAN −2.46 AVYEAVLR_460.8_587.4 PEPD_HUMAN −2.92

TABLE 33 Random Forest 32 Early Window Variable Protein MeanDecreaseGini ELIEELVNITQNQK_557.6_517.3 IL13_HUMAN 3.224369171 AHYDLR_387.7_288.2 FETUA_HUMAN 1.869007658 FSVVYAK_407.2_381.2 FETUA_HUMAN 1.770198171 ITLPDFTGDLR_624.3_288.2 LBP_HUMAN 1.710936472 ITGFLKPGK_320.9_301.2 LBP_HUMAN 1.623922439 ITGFLKPGK_320.9_429.3 LBP_HUMAN 1.408035272 ELIEELVNITQNQK_557.6_618.3 IL13_HUMAN 1.345412168 VFQFLEK_455.8_811.4 CO5_HUMAN 1.311332013 VQTAHFK_277.5_431.2 CO8A_HUMAN 1.308902373 FLNWIK_410.7_560.3 HABP2_HUMAN 1.308093745 DAGLSWGSAR_510.3_390.2 NEUR4_HUMAN 1.297033607 TLLPVSKPEIR_418.3_288.2 CO5_HUMAN 1.291280928 LIQDAVTGLTVNGQITGDK_972.0_798.4 ITIH3_HUMAN 1.28622301 QALEEFQK_496.8_680.3 CO8B_HUMAN 1.191731825 FSVVYAK_407.2_579.4 FETUA_HUMAN 1.078909138 ITLPDFTGDLR_624.3_920.5 LBP_HUMAN 1.072613747 AHYDLR_387.7_566.3 FETUA_HUMAN 1.029562263 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 1.00992071 DVLLLVHNLPQNLPGYFWYK_810.4_967.5 PSG9_HUMAN 1.007095529 SFRPFVPR_335.9_635.3 LBP_HUMAN 0.970312536 SDLEVAHYK_531.3_617.3 CO8B_HUMAN 0.967904893 VQEAHLTEDQIFYFPK_655.7_701.4 CO8G_HUMAN 0.960398254 VFQFLEK_455.8_276.2 CO5_HUMAN 0.931652095 SLLQPNK_400.2_599.4 CO8A_HUMAN 0.926470249 SFRPFVPR_335.9_272.2 LBP_HUMAN 0.911599611 FLNWIK_410.7_561.3 HABP2_HUMAN 0.852022868 LSSPAVITDK_515.8_743.4 PLMN_HUMAN 0.825455824 DVLLLVHNLPQNLPGYFWYK_810.4_594.3 PSG9_HUMAN 0.756797142 ALVLELAK_428.8_672.4 INHBE_HUMAN 0.748802555 DISEVVTPR_508.3_787.4 CFAB_HUMAN 0.733731518

TABLE 34 Random Forest 100 Early Window Variable Protein MeanDecreaseGini ELIEELVNITQNQK_557.6_517.3 IL13_HUMAN 1.709778508 LPNNVLQEK_527.8_844.5 AFAM_HUMAN 0.961692716 AHYDLR_387.7_288.2 FETUA_HUMAN 0.901586746 ITLPDFTGDLR_624.3_288.2 LBP_HUMAN 0.879119498 IEGNLIFDPNNYLPK_874.0_414.2 APOB_HUMAN 0.842483095 ITGFLKPGK_320.9_301.2 LBP_HUMAN 0.806905233 FSVVYAK_407.2_381.2 FETUA_HUMAN 0.790429706 ITGFLKPGK_320.9_429.3 LBP_HUMAN 0.710312386 VFQFLEK_455.8_811.4 CO5_HUMAN 0.709531553 LIQDAVTGLTVNGQITGDK_972.0_798.4 ITIH3_HUMAN 0.624325189 DADPDTFFAK_563.8_825.4 AFAM_HUMAN 0.618684313 FLNWIK_410.7_560.3 HABP2_HUMAN 0.617501242 TASDFITK_441.7_781.4 GELS_HUMAN 0.609275999 DAGLSWGSAR_510.3_390.2 NEUR4_HUMAN 0.588718595 VQTAHFK_277.5_431.2 CO8A_HUMAN 0.58669845 TLLPVSKPEIR_418.3_288.2 CO5_HUMAN 0.5670608 ELIEELVNITQNQK_557.6_618.3 IL13_HUMAN 0.555624783 TYLHTYESEI_628.3_908.4 ENPP2_HUMAN 0.537678415 HFQNLGK_422.2_527.2 AFAM_HUMAN 0.535543137 TASDFITK_441.7_710.4 GELS_HUMAN 0.532743323 ITLPDFTGDLR_624.3_920.5 LBP_HUMAN 0.51667902 QALEEFQK_496.8_680.3 CO8B_HUMAN 0.511314017 AVLHIGEK_289.5_348.7 THBG_HUMAN 0.510284122 FSVVYAK_407.2_579.4 FETUA_HUMAN 0.503907813 LPNNVLQEK_527.8_730.4 AFAM_HUMAN 0.501281631 AHYDLR_387.7_566.3 FETUA_HUMAN 0.474166711 IAPQLSTEELVSLGEK_857.5_333.2 AFAM_HUMAN 0.459595701 WWGGQPLWITATK_772.4_929.5 ENPP2_HUMAN 0.44680777 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 0.434157773 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN 0.432484862

TABLE 35 Random Forest Protein Early Window Variable Protein MeanDecreaseGini ELIEELVNITQNQK_557.6_517.3 IL13_HUMAN 2.881452809 LPNNVLQEK_527.8_844.5 AFAM_HUMAN 1.833987752 ITLPDFTGDLR_624.3_288.2 LBP_HUMAN 1.608843881 IEGNLIFDPNNYLPK_874.0_414.2 APOB_HUMAN 1.594658208 VFQFLEK_455.8_811.4 CO5_HUMAN 1.290134412 LIQDAVTGLTVNGQITGDK_972.0_798.4 ITIH3_HUMAN 1.167981736 TASDFITK_441.7_781.4 GELS_HUMAN 1.152847453 DAGLSWGSAR_510.3_390.2 NEUR4_HUMAN 1.146752656 FSVVYAK_407.2_579.4 FETUA_HUMAN 1.060168583 AVLHIGEK_289.5_348.7 THBG_HUMAN 1.033625773 FLNWIK_410.7_560.3 HABP2_HUMAN 1.022356789 QALEEFQK_496.8_680.3 CO8B_HUMAN 0.990074129 DVLLLVHNLPQNLPGYFWYK_810.4_967.5 PSG9_HUMAN 0.929633865 WWGGQPLWITATK_772.4_929.5 ENPP2_HUMAN 0.905895642 VQEAHLTEDQIFYFPK_655.7_701.4 CO8G_HUMAN 0.883887371 NNQLVAGYLQGPNVNLEEK_700.7_999.5 IL1RA_HUMAN 0.806472085 SLLQPNK_400.2_599.4 CO8A_HUMAN 0.783623222 DALSSVQESQVAQQAR_573.0_672.4 APOC3_HUMAN 0.774365756 NIQSVNVK_451.3_674.4 GROA_HUMAN 0.767963386 HPWIVHWDQLPQYQLNR_744.0_1047.0 KS6A3_HUMAN 0.759960139 TTSDGGYSFK_531.7_860.4 INHA_HUMAN 0.732813448 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 0.718779092 LSSPAVITDK_515.8_743.4 PLMN_HUMAN 0.699547739 TGVAVNKPAEFTVDAK_549.6_258.1 FLNA_HUMAN 0.693159192 TLNAYDHR_330.5_312.2 PAR3_HUMAN 0.647300964 DISEVVTPR_508.3_787.4 CFAB_HUMAN 0.609165621 LIENGYFHPVK_439.6_627.4 F13B_HUMAN 0.60043345 SGVDLADSNQK_567.3_662.3 VGFR3_HUMAN 0.596079858 ALQDQLVLVAAK_634.9_289.2 ANGT_HUMAN 0.579034994 ALVLELAK_428.8_672.4 INHBE_HUMAN 0.573458483

TABLE 36 Random Forest All Early Window Variable Protein MeanDecreaseGini ELIEELVNITQNQK_557.6_517.3 IL13_HUMAN 0.730972421 ITLPDFTGDLR_624.3_288.2 LBP_HUMAN 0.409808774 AHYDLR_387.7_288.2 FETUA_HUMAN 0.409298983 FSVVYAK_407.2_381.2 FETUA_HUMAN 0.367730833 ITGFLKPGK_320.9_301.2 LBP_HUMAN 0.350485117 VFQFLEK_455.8_811.4 CO5_HUMAN 0.339289475 ELIEELVNITQNQK_557.6_618.3 IL13_HUMAN 0.334303166 LPNNVLQEK_527.8_844.5 AFAM_HUMAN 0.329800706 IEGNLIFDPNNYLPK_874.0_414.2 APOB_HUMAN 0.325596677 ITGFLKPGK_320.9_429.3 LBP_HUMAN 0.31473104 FLNWIK_410.7_560.3 HABP2_HUMAN 0.299810081 LIQDAVTGLTVNGQITGDK_972.0_798.4 ITIH3_HUMAN 0.295613448 ITLPDFTGDLR_624.3_920.5 LBP_HUMAN 0.292212699 DAGLSWGSAR_510.3_390.2 NEUR4_HUMAN 0.285812225 TLLPVSKPEIR_418.3_288.2 CO5_HUMAN 0.280857718 FSVVYAK_407.2_579.4 FETUA_HUMAN 0.278531322 DADPDTFFAK_563.8_825.4 AFAM_HUMAN 0.258938798 AHYDLR_387.7_566.3 FETUA_HUMAN 0.256160046 QALEEFQK_496.8_680.3 CO8B_HUMAN 0.245543641 HTLNQIDEVK_598.8_951.5 FETUA_HUMAN 0.239528081 TASDFITK_441.7_781.4 GELS_HUMAN 0.227485958 VFQFLEK_455.8_276.2 CO5_HUMAN 0.226172392 DVLLLVHNLPQNLPGYFWYK_810.4_967.5 PSG9_HUMAN 0.218613384 VQTAHFK_277.5_431.2 CO8A_HUMAN 0.217171548 SFRPFVPR_335.9_635.3 LBP_HUMAN 0.214798112 HFQNLGK_422.2_527.2 AFAM_HUMAN 0.211756476 SVSLPSLDPASAK_636.4_473.3 APOB_HUMAN 0.211319422 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 0.206574494 HFQNLGK_422.2_285.1 AFAM_HUMAN 0.204024196 AVLHIGEK_289.5_348.7 THBG_HUMAN 0.201102917

TABLE 37 Random Forest SummedGini Early Window Transition Protein SumBestGini ELIEELVNITQNQK_557.6_517.3 IL13_HUMAN 242.5373659 VFQFLEK_455.8_811.4 CO5_HUMAN 115.1113943 FLNWIK_410.7_560.3 HABP2_HUMAN 107.4572447 ITLPDFTGDLR_624.3_288.2 LBP_HUMAN 104.0742727 LIQDAVTGLTVNGQITGDK_972.0_798.4 ITIH3_HUMAN 103.3238077 DAGLSWGSAR_510.3_390.2 NEUR4_HUMAN 70.4151533 AHYDLR_387.7_288.2 FETUA_HUMAN 140.2670822 FSVVYAK_407.2_381.2 FETUA_HUMAN 121.3664352 LPNNVLQEK_527.8_844.5 AFAM_HUMAN 115.5211679 ITGFLKPGK_320.9_429.3 LBP_HUMAN 114.9512704 ITGFLKPGK_320.9_301.2 LBP_HUMAN 112.916627 IEGNLIFDPNNYLPK_874.0_414.2 APOB_HUMAN 52.21169288 VQTAHFK_277.5_431.2 CO8A_HUMAN 144.5237215 TLLPVSKPEIR_418.3_288.2 CO5_HUMAN 96.16982897 QALEEFQK_496.8_680.3 CO8B_HUMAN 85.35050759 FSVVYAK_407.2_579.4 FETUA_HUMAN 73.23969945 ELIEELVNITQNQK_557.6_618.3 IL13_HUMAN 61.61450671 TASDFITK_441.7_781.4 GELS_HUMAN 61.32155633 DVLLLVHNLPQNLPGYFWYK_810.4_967.5 PSG9_HUMAN 99.68404123 AVLHIGEK_289.5_348.7 THBG_HUMAN 69.96748485 ITLPDFTGDLR_624.3_920.5 LBP_HUMAN 56.66810872 WWGGQPLWITATK_772.4_929.5 ENPP2_HUMAN 56.54173176 VQEAHLTEDQIFYFPK_655.7_701.4 CO8G_HUMAN 47.92505575 DADPDTFFAK_563.8_825.4 AFAM_HUMAN 40.34147696 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN 145.0311483 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 109.4072996 FLPCENK_454.2_550.2 IL10_HUMAN 105.7756691 VQTAHFK_277.5_502.3 CO8A_HUMAN 101.5877845 VFQFLEK_455.8_276.2 CO5_HUMAN 95.71159157 TYLHTYESEI_628.3_908.4 ENPP2_HUMAN 94.92157517 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 90.67568777 NKPGVYTDVAYYLAWIR_677.0_545.3 FA12_HUMAN 90.35890105 LEEHYELR_363.5_580.3 PAI2_HUMAN 88.44833508 HPWIVHWDQLPQYQLNR_744.0_1047.0 KS6A3_HUMAN 88.37680942 HTLNQIDEVK_598.8_951.5 FETUA_HUMAN 87.63064143 LPNNVLQEK_527.8_730.4 AFAM_HUMAN 86.64484642 ALDLSLK_380.2_575.3 ITIH3_HUMAN 83.51201287 YGIEEHGK_311.5_599.3 CXA1_HUMAN 82.47620831 LSSPAVITDK_515.8_830.5 PLMN_HUMAN 81.5433587 LEEHYELR_363.5_288.2 PAI2_HUMAN 79.01571985 NVIQISNDLENLR_509.9_402.3 LEP_HUMAN 78.86670236 SGFSFGFK_438.7_732.4 CO8B_HUMAN 78.71961929 SDLEVAHYK_531.3_617.3 CO8B_HUMAN 78.24005567 NADYSYSVWK_616.8_333.2 CO5_HUMAN 76.07974354 AHYDLR_387.7_566.3 FETUA_HUMAN 74.68253347 GAVHVVVAETDYQSFAVLYLER_822.8_580.3 CO8G_HUMAN 73.75860248 LIENGYFHPVK_439.6_627.4 F13B_HUMAN 73.74965194 ALDLSLK_380.2_185.1 ITIH3_HUMAN 72.760739 WWGGQPLWITATK_772.4_373.2 ENPP2_HUMAN 72.51936706 FGFGGSTDSGPIR_649.3_946.5 ADA12_HUMAN 72.49183198 GLQYAAQEGLLALQSELLR_1037.1_929.5 LBP_HUMAN 67.17588648 HFQNLGK_422.2_527.2 AFAM_HUMAN 66.11702719 YSHYNER_323.5_581.3 HABP2_HUMAN 65.56238612 ISQGEADINIAFYQR_575.6_684.4 MMP8_HUMAN 65.50301246 TGVAVNKPAEFTVDAK_549.6_258.1 FLNA_HUMAN 64.85259525 NIQSVNVK_451.3_674.4 GROA_HUMAN 64.53010225 DALSSVQESQVAQQAR_573.0_672.4 APOC3_HUMAN 64.12149927 SLLQPNK_400.2_599.4 CO8A_HUMAN 62.68167847 SFRPFVPR_335.9_635.3 LBP_HUMAN 61.90157662 NNQLVAGYLQGPNVNLEEK_700.7_999.5 IL1RA_HUMAN 61.54435815 LYYGDDEK_501.7_563.2 CO8A_HUMAN 60.16700473 SWNEPLYHLVTEVR_581.6_716.4 PRL_HUMAN 59.78209065 SGVDLADSNQK_567.3_662.3 VGFR3_HUMAN 58.93982896 GTYLYNDCPGPGQDTDCR_697.0_335.2 TNR1A_HUMAN 58.72963941 HATLSLSIPR_365.6_472.3 VGFR3_HUMAN 57.98669834 FIVGFTR_420.2_261.2 CCL20_HUMAN 57.23165578 QNYHQDSEAAINR_515.9_544.3 FRIH_HUMAN 57.21116697 DVLLLVHNLPQNLPGYFWYK_810.4_594.3 PSG9_HUMAN 56.84150484 FLNWIK_410.7_561.3 HABP2_HUMAN 56.37258274 SLQAFVAVAAR_566.8_487.3 IL23A_HUMAN 56.09012981 HFQNLGK_422.2_285.1 AFAM_HUMAN 56.04480022 GPGEDFR_389.2_322.2 PTGDS_HUMAN 55.7583763 NKPGVYTDVAYYLAWIR_677.0_821.5 FA12_HUMAN 55.53857645 LIQDAVTGLTVNGQITGDK_972.0_640.4 ITIH3_HUMAN 55.52577583 YYGYTGAFR_549.3_450.3 TRFL_HUMAN 54.27147366 TLNAYDHR_330.5_312.2 PAR3_HUMAN 54.19190934 IQTHSTTYR_369.5_627.3 F13B_HUMAN 54.18950583 TASDFITK_441.7_710.4 GELS_HUMAN 54.1056456 ALNHLPLEYNSALYSR_621.0_696.4 CO6_HUMAN 53.8997252 DADPDTFFAK_563.8_302.1 AFAM_HUMAN 53.85914848 SVSLPSLDPASAK_636.4_473.3 APOB_HUMAN 53.41996191 TTSDGGYSFK_531.7_860.4 INHA_HUMAN 52.24655536 AFTECCVVASQLR_770.9_574.3 CO5_HUMAN 51.67853429 ELPQSIVYK_538.8_409.2 FBLN3_HUMAN 51.35853002 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 51.23842124 FQLSETNR_497.8_605.3 PSG2_HUMAN 51.01576848 GSLVQASEANLQAAQDFVR_668.7_806.4 ITIH1_HUMAN 50.81923338 FSLVSGWGQLLDR_493.3_403.2 FA7_HUMAN 50.54425114 ECEELEEK_533.2_405.2 IL15_HUMAN 50.41977421 NADYSYSVWK_616.8_769.4 CO5_HUMAN 50.36434595 SLLQPNK_400.2_358.2 CO8A_HUMAN 49.75593162 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 49.43389721 DISEVVTPR_508.3_787.4 CFAB_HUMAN 49.00234897 AEVIWTSSDHQVLSGK_586.3_300.2 PD1L1_HUMAN 48.79028835 SGVDLADSNQK_567.3_591.3 VGFR3_HUMAN 48.70665587 SILFLGK_389.2_201.1 THBG_HUMAN 48.5997957 AVLHIGEK_289.5_292.2 THBG_HUMAN 48.4605866 QLYGDTGVLGR_589.8_501.3 CO8G_HUMAN 48.11414904 FSLVSGWGQLLDR_493.3_516.3 FA7_HUMAN 47.59635333 DSPVLIDFFEDTER_841.9_399.2 HRG_HUMAN 46.83840473 INPASLDK_429.2_630.4 C163A_HUMAN 46.78947931 GAVHVVVAETDYQSFAVLYLER_822.8_863.5 CO8G_HUMAN 46.66185339 FLQEQGHR_338.8_497.3 CO8G_HUMAN 46.64415952 LNIGYIEDLK_589.3_837.4 PAI2_HUMAN 46.5879123 LSSPAVITDK_515.8_743.4 PLMN_HUMAN 46.2857838 GLQYAAQEGLLALQSELLR_1037.1_858.5 LBP_HUMAN 45.7427767 SDGAKPGPR_442.7_213.6 COLI_HUMAN 45.27828366 GYQELLEK_490.3_502.3 FETA_HUMAN 43.52928868 GGEGTGYFVDFSVR_745.9_869.5 HRG_HUMAN 43.24514327 ADLFYDVEALDLESPK_913.0_447.2 HRG_HUMAN 42.56268679 ADLFYDVEALDLESPK_913.0_331.2 HRG_HUMAN 42.48967422 EAQLPVIENK_570.8_699.4 PLMN_HUMAN 42.21213429 SILFLGK_389.2_577.4 THBG_HUMAN 42.03379581 HTLNQIDEVK_598.8_958.5 FETUA_HUMAN 41.98377176 AQPVQVAEGSEPDGFWEALGGK_758.0_574.3 GELS_HUMAN 41.89547273 FLPCENK_454.2_390.2 IL10_HUMAN 41.66612478 LIEIANHVDK_384.6_498.3 ADA12_HUMAN 41.50878046 DEIPHNDIALLK_459.9_510.8 HABP2_HUMAN 41.27830935 SLQAFVAVAAR_566.8_804.5 IL23A_HUMAN 41.00430596 YISPDQLADLYK_713.4_277.2 ENOA_HUMAN 40.90053801 SLPVSDSVLSGFEQR_810.9_836.4 CO8G_HUMAN 40.62020941 DGSPDVTTADIGANTPDATK_973.5_531.3 PGRP2_HUMAN 40.33913091 NTGVISVVTTGLDR_716.4_662.4 CADH1_HUMAN 40.05291612 ALVLELAK_428.8_672.4 INHBE_HUMAN 40.01646465 YEFLNGR_449.7_293.1 PLMN_HUMAN 39.83344278 WGAAPYR_410.7_577.3 PGRP2_HUMAN 39.52766213 TFLTVYWTPER_706.9_401.2 ICAM1_HUMAN 39.13662034 SEYGAALAWEK_612.8_845.5 CO6_HUMAN 38.77511119 VGVISFAQK_474.8_693.4 TFR2_HUMAN 38.5823457 IIEVEEEQEDPYLNDR_996.0_777.4 FBLN1_HUMAN 38.30913304 TGYYFDGISR_589.8_694.4 FBLN1_HUMAN 38.30617106 LQGTLPVEAR_542.3_571.3 CO5_HUMAN 37.93064544 DSPVLIDFFEDTER_841.9_512.3 HRG_HUMAN 37.4447737 AALAAFNAQNNGSNFQLEEISR_789.1_746.4 FETUA_HUMAN 37.02483715 DGSPDVTTADIGANTPDATK_973.5_844.4 PGRP2_HUMAN 36.59864788 ILILPSVTR_506.3_785.5 PSGx_HUMAN 36.43814815 SVSLPSLDPASAK_636.4_885.5 APOB_HUMAN 36.27689491 TLAFVR_353.7_492.3 FA7_HUMAN 36.18771771 VAPGVANPGTPLA_582.3_555.3 A6NIT4_HUMAN 35.70677357 HELTDEELQSLFTNFANVVDK_817.1_906.5 AFAM_HUMAN 35.14441609 AGLLRPDYALLGHR_518.0_369.2 PGRP2_HUMAN 35.13047098 GDTYPAELYITGSILR_885.0_1332.8 F13B_HUMAN 34.97832404 LFIPQITR_494.3_727.4 PSG9_HUMAN 34.76811249 GYQELLEK_490.3_631.4 FETA_HUMAN 34.76117605 VSEADSSNADWVTK_754.9_533.3 CFAB_HUMAN 34.49787512 LNIGYIEDLK_589.3_950.5 PAI2_HUMAN 34.48448691 SFRPFVPR_335.9_272.2 LBP_HUMAN 34.27529415 ILDGGNK_358.7_490.2 CXCL5_HUMAN 34.2331388 EANQSTLENFLER_775.9_678.4 IL4_HUMAN 34.14295797 DFNQFSSGEK_386.8_189.1 FETA_HUMAN 34.05459951 IEEIAAK_387.2_660.4 CO5_HUMAN 33.93778148 TEFLSNYLTNVDDITLVPGTLGR_846.8_600.3 ENPP2_HUMAN 33.87864446 LPATEKPVLLSK_432.6_347.2 HYOU1_HUMAN 33.69005522 FLQEQGHR_338.8_369.2 CO8G_HUMAN 33.61179024 APLTKPLK_289.9_357.2 CRP_HUMAN 33.59900279 YSHYNER_323.5_418.2 HABP2_HUMAN 33.50888447 TSYQVYSK_488.2_787.4 C163A_HUMAN 33.11650018 IALGGLLFPASNLR_481.3_657.4 SHBG_HUMAN 33.02974341 TGISPLALIK_506.8_741.5 APOB_HUMAN 32.64471573 LYYGDDEK_501.7_726.3 CO8A_HUMAN 32.60782458 IVLSLDVPIGLLQILLEQAR_735.1_503.3 UCN2_HUMAN 32.37907686 EAQLPVIENK_570.8_329.2 PLMN_HUMAN 32.34049256 TGYYFDGISR_589.8_857.4 FBLN1_HUMAN 32.14526507 VGVISFAQK_474.8_580.3 TFR2_HUMAN 32.11753213 FQSVFTVTR_542.8_623.4 C1QC_HUMAN 32.11360444 TSDQIHFFFAK_447.6_659.4 ANT3_HUMAN 31.95867038 IAPQLSTEELVSLGEK_857.5_333.2 AFAM_HUMAN 31.81531364 EVFSKPISWEELLQ_852.9_260.2 FA40A_HUMAN 31.36698726 DEIPHNDIALLK_459.9_260.2 HABP2_HUMAN 31.1839869 NYFTSVAHPNLFIATK_608.3_319.2 IL1A_HUMAN 31.09867061 ITENDIQIALDDAK_779.9_632.3 APOB_HUMAN 30.77026845 DTYVSSFPR_357.8_272.2 TCEA1_HUMAN 30.67784731 TDAPDLPEENQAR_728.3_843.4 CO5_HUMAN 30.66251941 LFYADHPFIFLVR_546.6_647.4 SERPH_HUMAN 30.65831566 TEQAAVAR_423.2_487.3 FA12_HUMAN 30.44356842 AVGYLITGYQR_620.8_737.4 PZP_HUMAN 30.36425528 HSHESQDLR_370.2_288.2 HRG_HUMAN 30.34684703 IALGGLLFPASNLR_481.3_412.3 SHBG_HUMAN 30.34101643 IAQYYYTFK_598.8_884.4 F13B_HUMAN 30.23453833 SLPVSDSVLSGFEQR_810.9_723.3 CO8G_HUMAN 30.11396489 IIGGSDADIK_494.8_762.4 C1S_HUMAN 30.06572687 QTLSWTVTPK_580.8_545.3 PZP_HUMAN 30.04139865 HYFIAAVER_553.3_658.4 FA8_HUMAN 29.80239884 QVCADPSEEWVQK_788.4_374.2 CCL3_HUMAN 29.61435573 DLHLSDVFLK_396.2_366.2 CO6_HUMAN 29.60077507 NIQSVNVK_451.3_546.3 GROA_HUMAN 29.47619619 QTLSWTVTPK_580.8_818.4 PZP_HUMAN 29.40047934 HSHESQDLR_370.2_403.2 HRG_HUMAN 29.32242262 LLEVPEGR_456.8_356.2 C1S_HUMAN 29.14169137 LIENGYFHPVK_439.6_343.2 F13B_HUMAN 28.63056809 EDTPNSVWEPAK_686.8_630.3 C1S_HUMAN 28.61352686 AFTECCVVASQLR_770.9_673.4 CO5_HUMAN 28.57830281 VNHVTLSQPK_374.9_459.3 B2MG_HUMAN 28.27203693 VSFSSPLVAISGVALR_802.0_715.4 PAPP1_HUMAN 28.13008712 DPDQTDGLGLSYLSSHIANVER_796.4_456.2 GELS_HUMAN 28.06549895 VVGGLVALR_442.3_784.5 FA12_HUMAN 28.00684006 NEIVFPAGILQAPFYTR_968.5_357.2 ECE1_HUMAN 27.97758456 QVCADPSEEWVQK_788.4_275.2 CCL3_HUMAN 27.94276837 LQDAGVYR_461.2_680.3 PD1L1_HUMAN 27.88063261 IQTHSTTYR_369.5_540.3 F13B_HUMAN 27.68873826 TPSAAYLWVGTGASEAEK_919.5_849.4 GELS_HUMAN 27.66889639 ALALPPLGLAPLLNLWAKPQGR_770.5_256.2 SHBG_HUMAN 27.63105727 ALQDQLVLVAAK_634.9_289.2 ANGT_HUMAN 27.63097319 IEEIAAK_387.2_531.3 CO5_HUMAN 27.52427934 TAVTANLDIR_537.3_288.2 CHL1_HUMAN 27.44246841 VSEADSSNADWVTK_754.9_347.2 CFAB_HUMAN 27.43976782 ITENDIQIALDDAK_779.9_873.5 APOB_HUMAN 27.39263522 SSNNPHSPIVEEFQVPYNK_729.4_521.3 C1S_HUMAN 27.34493617 HPWIVHWDQLPQYQLNR_744.0_918.5 KS6A3_HUMAN 27.19681613 TPSAAYLWVGTGASEAEK_919.5_428.2 GELS_HUMAN 27.17319953 AFLEVNEEGSEAAASTAVVIAGR_764.4_614.4 ANT3_HUMAN 27.10487351 WGAAPYR_410.7_634.3 PGRP2_HUMAN 27.09930054 IEVNESGTVASSSTAVIVSAR_693.0_545.3 PAI1_HUMAN 27.02567296 AEAQAQYSAAVAK_654.3_908.5 ITIH4_HUMAN 26.98305259 VPLALFALNR_557.3_917.6 PEPD_HUMAN 26.96988826 TLEAQLTPR_514.8_685.4 HEP2_HUMAN 26.94672621 QALEEFQK_496.8_551.3 CO8B_HUMAN 26.67037155 WNFAYWAAHQPWSR_607.3_545.3 PRG2_HUMAN 26.62600679 IYLQPGR_423.7_570.3 ITIH2_HUMAN 26.58752589 FFQYDTWK_567.8_840.4 IGF2_HUMAN 26.39942037 NEIWYR_440.7_357.2 FA12_HUMAN 26.35177282 GGEGTGYFVDFSVR_745.9_722.4 HRG_HUMAN 26.31688167 VGEYSLYIGR_578.8_708.4 SAMP_HUMAN 26.17367498 TAHISGLPPSTDFIVYLSGLAPSIR_871.5_800.5 TENA_HUMAN 26.13688183 GVTGYFTFNLYLK_508.3_260.2 PSG5_HUMAN 26.06007032 DYWSTVK_449.7_620.3 APOC3_HUMAN 26.03765187 YENYTSSFFIR_713.8_756.4 IL12B_HUMAN 25.9096605 YGLVTYATYPK_638.3_334.2 CFAB_HUMAN 25.84440452 LFIPQITR_494.3_614.4 PSG9_HUMAN 25.78081129 YEFLNGR_449.7_606.3 PLMN_HUMAN 25.17159874 SEPRPGVLLR_375.2_454.3 FA7_HUMAN 25.16444381 NSDQEIDFK_548.3_294.2 S10A5_HUMAN 25.12266401 YEVQGEVFTKPQLWP_911.0_293.1 CRP_HUMAN 24.77595195 GVTGYFTFNLYLK_508.3_683.9 PSG5_HUMAN 24.75289081 ISLLLIESWLEPVR_834.5_371.2 CSH_HUMAN 24.72379326 ALLLGWVPTR_563.3_373.2 PAR4_HUMAN 24.68096599 VNHVTLSQPK_374.9_244.2 B2MG_HUMAN 24.53420489 SGAQATWTELPWPHEK_613.3_793.4 HEMO_HUMAN 24.25610995 AQPVQVAEGSEPDGFWEALGGK_758.0_623.4 GELS_HUMAN 24.18769142 DLPHITVDR_533.3_490.3 MMP7_HUMAN 24.02606052 SEYGAALAWEK_612.8_788.4 CO6_HUMAN 24.00163743 AVGYLITGYQR_620.8_523.3 PZP_HUMAN 23.93958524 GFQALGDAADIR_617.3_717.4 TIMP1_HUMAN 23.69249513 YEVQGEVFTKPQLWP_911.0_392.2 CRP_HUMAN 23.67764212 SDGAKPGPR_442.7_459.2 COLI_HUMAN 23.63551614 GFQALGDAADIR_617.3_288.2 TIMP1_HUMAN 23.55832742 IAPQLSTEELVSLGEK_857.5_533.3 AFAM_HUMAN 23.38139357 DTDTGALLFIGK_625.8_217.1 PEDF_HUMAN 23.33375418 LHEAFSPVSYQHDLALLR_699.4_380.2 FA12_HUMAN 23.27455931 IYLQPGR_423.7_329.2 ITIH2_HUMAN 23.19122626

TABLE 38 Random Forest 32 Middle Window Variable UniProt_ID MeanDecreaseGini SEYGAALAWEK_612.8_788.4 CO6_HUMAN 2.27812193 LLAPSDSPEWLSFDVTGVVR_730.1_430.3 TGFB1_HUMAN 2.080133179 ALNHLPLEYNSALYSR_621.0_696.4 CO6_HUMAN 1.952233942 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN 1.518833357 VEHSDLSFSK_383.5_234.1 B2MG_HUMAN 1.482593086 VFQFLEK_455.8_811.4 CO5_HUMAN 1.448810425 VNHVTLSQPK_374.9_244.2 B2MG_HUMAN 1.389922815 YGIEEHGK_311.5_599.3 CXA1_HUMAN 1.386794676 TLAFVR_353.7_492.3 FA7_HUMAN 1.371530925 VLEPTLK_400.3_587.3 VTDB_HUMAN 1.368583173 VLEPTLK_400.3_458.3 VTDB_HUMAN 1.336029064 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN 1.307024357 AQPVQVAEGSEPDGFWEALGGK_758.0_574.3 GELS_HUMAN 1.282930911 LHEAFSPVSYQHDLALLR_699.4_251.2 FA12_HUMAN 1.25362163 SEPRPGVLLR_375.2_654.4 FA7_HUMAN 1.205539225 VEHSDLSFSK_383.5_468.2 B2MG_HUMAN 1.201047302 SLDFTELDVAAEK_719.4_316.2 ANGT_HUMAN 1.189617326 SEYGAALAWEK_612.8_845.5 CO6_HUMAN 1.120706696 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 1.107036657 VNHVTLSQPK_374.9_459.3 B2MG_HUMAN 1.083264902 IEEIAAK_387.2_660.4 CO5_HUMAN 1.043635292 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 0.962643698 TLLPVSKPEIR_418.3_514.3 CO5_HUMAN 0.933440467 TEQAAVAR_423.2_615.4 FA12_HUMAN 0.878933553 DLHLSDVFLK_396.2_260.2 CO6_HUMAN 0.816855601 ALQDQLVLVAAK_634.9_289.2 ANGT_HUMAN 0.812620232 SLQAFVAVAAR_566.8_804.5 IL23A_HUMAN 0.792274782 QGHNSVFLIK_381.6_260.2 HEMO_HUMAN 0.770830031 ALQDQLVLVAAK_634.9_956.6 ANGT_HUMAN 0.767468246 SLDFTELDVAAEK_719.4_874.5 ANGT_HUMAN 0.745827911

TABLE 39 Random Forest 100 Middle Window Variable UniProt_ID MeanDecreaseGini SEYGAALAWEK_612.8_788.4 CO6_HUMAN 1.241568411 ALNHLPLEYNSALYSR_621.0_696.4 CO6_HUMAN 0.903126414 LLAPSDSPEWLSFDVTGVVR_730.1_430.3 TGFB1_HUMAN 0.846216563 ANLINNIFELAGLGK_793.9_299.2 LCAP_HUMAN 0.748261193 VFQFLEK_455.8_811.4 CO5_HUMAN 0.717545171 VEHSDLSFSK_383.5_234.1 B2MG_HUMAN 0.683219617 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN 0.671091545 LNIGYIEDLK_589.3_950.5 PAI2_HUMAN 0.652293621 VLEPTLK_400.3_587.3 VTDB_HUMAN 0.627095631 VNHVTLSQPK_374.9_244.2 B2MG_HUMAN 0.625773888 VLEPTLK_400.3_458.3 VTDB_HUMAN 0.613655529 AQPVQVAEGSEPDGFWEALGGK_758.0_574.3 GELS_HUMAN 0.576305627 TLFIFGVTK_513.3_811.5 PSG4_HUMAN 0.574056825 YGIEEHGK_311.5_599.3 CXA1_HUMAN 0.570270447 VPLALFALNR_557.3_620.4 PEPD_HUMAN 0.556087614 EVFSKPISWEELLQ_852.9_376.2 FA40A_HUMAN 0.531461012 VEHSDLSFSK_383.5_468.2 B2MG_HUMAN 0.531214597 TLAFVR_353.7_492.3 FA7_HUMAN 0.53070743 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN 0.521633041 SEYGAALAWEK_612.8_845.5 CO6_HUMAN 0.514509661 SLDFTELDVAAEK_719.4_316.2 ANGT_HUMAN 0.50489698 SEPRPGVLLR_375.2_654.4 FA7_HUMAN 0.4824926 LHEAFSPVSYQHDLALLR_699.4_251.2 FA12_HUMAN 0.48217238 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 0.472286273 AVDIPGLEAATPYR_736.9_399.2 TENA_HUMAN 0.470892051 FSLVSGWGQLLDR_493.3_403.2 FA7_HUMAN 0.465839813 GEVTYTTSQVSK_650.3_750.4 EGLN_HUMAN 0.458736205 VNHVTLSQPK_374.9_459.3 B2MG_HUMAN 0.454348892 HFQNLGK_422.2_527.2 AFAM_HUMAN 0.45127405 YGIEEHGK_311.5_341.2 CXA1_HUMAN 0.430641646

TABLE 40 Random Forest Protein Middle Window Variable UniProt_ID MeanDecreaseGini SEYGAALAWEK_612.8_788.4 CO6_HUMAN 2.09649626 LLAPSDSPEWLSFDVTGVVR_730.1_430.3 TGFB1_HUMAN 1.27664656 VFQFLEK_455.8_811.4 CO5_HUMAN 1.243884833 ANLINNIFELAGLGK_793.9_299.2 LCAP_HUMAN 1.231814882 VEHSDLSFSK_383.5_234.1 B2MG_HUMAN 1.188808078 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN 1.185075445 LNIGYIEDLK_589.3_950.5 PAI2_HUMAN 1.122351536 VLEPTLK_400.3_458.3 VTDB_HUMAN 1.062664798 VPLALFALNR_557.3_620.4 PEPD_HUMAN 1.019466776 TLAFVR_353.7_492.3 FA7_HUMAN 0.98797064 TLFIFGVTK_513.3_811.5 PSG4_HUMAN 0.980159531 AQPVQVAEGSEPDGFWEALGGK_758.0_574.3 GELS_HUMAN 0.960286027 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN 0.947091926 YGIEEHGK_311.5_599.3 CXA1_HUMAN 0.946937719 EVFSKPISWEELLQ_852.9_376.2 FA40A_HUMAN 0.916262164 LHEAFSPVSYQHDLALLR_699.4_251.2 FA12_HUMAN 0.891310053 SLDFTELDVAAEK_719.4_316.2 ANGT_HUMAN 0.884498494 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 0.869043942 HFQNLGK_422.2_527.2 AFAM_HUMAN 0.865435217 AVDIPGLEAATPYR_736.9_399.2 TENA_HUMAN 0.844842109 TLNAYDHR_330.5_312.2 PAR3_HUMAN 0.792615068 DVLLLVHNLPQNLTGHIWYK_791.8_310.2 PSG7_HUMAN 0.763629346 GPITSAAELNDPQSILLR_632.4_826.5 EGLN_HUMAN 0.762305265 VVLSSGSGPGLDLPLVLGLPLQLK_791.5_598.4 SHBG_HUMAN 0.706312721 SLQNASAIESILK_687.4_860.5 IL3_HUMAN 0.645503581 HYINLITR_515.3_301.1 NPY_HUMAN 0.62631682 VELAPLPSWQPVGK_760.9_342.2 ICAM1_HUMAN 0.608991877 LQVNTPLVGASLLR_741.0_925.6 BPIA1_HUMAN 0.607801279 TLEAQLTPR_514.8_814.4 HEP2_HUMAN 0.597771074 SDGAKPGPR_442.7_459.2 COLI_HUMAN 0.582773073

TABLE 41 Random Forest All Middle Window Variable UniProt_ID MeanDecreaseGini SEYGAALAWEK_612.8_788.4 CO6_HUMAN 0.493373282 ALNHLPLEYNSALYSR_621.0_696.4 CO6_HUMAN 0.382180772 VFQFLEK_455.8_811.4 CO5_HUMAN 0.260292083 LLAPSDSPEWLSFDVTGVVR_730.1_430.3 TGFB1_HUMAN 0.243156718 NADYSYSVWK_616.8_769.4 CO5_HUMAN 0.242388196 VLEPTLK_400.3_458.3 VTDB_HUMAN 0.238171849 VEHSDLSFSK_383.5_234.1 B2MG_HUMAN 0.236873731 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN 0.224727161 VLEPTLK_400.3_587.3 VTDB_HUMAN 0.222105614 TLFIFGVTK_513.3_811.5 PSG4_HUMAN 0.210807574 ANLINNIFELAGLGK_793.9_299.2 LCAP_HUMAN 0.208714978 LNIGYIEDLK_589.3_950.5 PAI2_HUMAN 0.208027555 SEYGAALAWEK_612.8_845.5 CO6_HUMAN 0.197362212 VNHVTLSQPK_374.9_244.2 B2MG_HUMAN 0.195728091 YGIEEHGK_311.5_599.3 CXA1_HUMAN 0.189969499 HFQNLGK_422.2_527.2 AFAM_HUMAN 0.189572857 AGITIPR_364.2_486.3 IL17_HUMAN 0.188351054 AQPVQVAEGSEPDGFWEALGGK_758.0_574.3 GELS_HUMAN 0.185069517 SLDFTELDVAAEK_719.4_316.2 ANGT_HUMAN 0.173688295 TLAFVR_353.7_492.3 FA7_HUMAN 0.170636045 SEPRPGVLLR_375.2_654.4 FA7_HUMAN 0.170608352 TLLIANETLR_572.3_703.4 IL5_HUMAN 0.16745571 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 0.161514946 LHEAFSPVSYQHDLALLR_699.4_251.2 FA12_HUMAN 0.15852146 DGSPDVTTADIGANTPDATK_973.5_844.4 PGRP2_HUMAN 0.154028378 VPLALFALNR_557.3_620.4 PEPD_HUMAN 0.153725879 AVDIPGLEAATPYR_736.9_399.2 TENA_HUMAN 0.150920884 YGIEEHGK_311.5_341.2 CXA1_HUMAN 0.150319671 FSLVSGWGQLLDR_493.3_403.2 FA7_HUMAN 0.144781622 IEEIAAK_387.2_660.4 CO5_HUMAN 0.141983196

TABLE 42 Random Forest 32 Middle-Late Window Variable UniProt_ID MeanDecreaseGini VPLALFALNR_557.3_620.4 PEPD_HUMAN 4.566619475 VFQFLEK_455.8_811.4 CO5_HUMAN 3.062474666 AQPVQVAEGSEPDGFWEALGGK_758.0_574.3 GELS_HUMAN 3.033740627 LIEIANHVDK_384.6_498.3 ADA12_HUMAN 2.825082394 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN 2.787777983 TLAFVR_353.7_492.3 FA7_HUMAN 2.730532075 ALNHLPLEYNSALYSR_621.0_696.4 CO6_HUMAN 2.671290375 AVYEAVLR_460.8_587.4 PEPD_HUMAN 2.621357053 SEPRPGVLLR_375.2_654.4 FA7_HUMAN 2.57568964 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 2.516708906 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 2.497348374 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 2.457401462 YGIEEHGK_311.5_599.3 CXA1_HUMAN 2.396824268 VLEPTLK_400.3_587.3 VTDB_HUMAN 2.388105564 SEYGAALAWEK_612.8_788.4 CO6_HUMAN 2.340473883 WSAGLTSSQVDLYIPK_883.0_515.3 CBG_HUMAN 2.332007976 FGFGGSTDSGPIR_649.3_946.5 ADA12_HUMAN 2.325669514 SEYGAALAWEK_612.8_845.5 CO6_HUMAN 2.31761671 QINSYVK_426.2_496.3 CBG_HUMAN 2.245221163 QINSYVK_426.2_610.3 CBG_HUMAN 2.212307699 TEQAAVAR_423.2_615.4 FA12_HUMAN 2.105860336 AVYEAVLR_460.8_750.4 PEPD_HUMAN 2.098321893 TEQAAVAR_423.2_487.3 FA12_HUMAN 2.062684763 DFNQFSSGEK_386.8_333.2 FETA_HUMAN 2.05160689 SLQAFVAVAAR_566.8_804.5 IL23A_HUMAN 1.989521006 SLDFTELDVAAEK_719.4_316.2 ANGT_HUMAN 1.820628782 DPTFIPAPIQAK_433.2_556.3 ANGT_HUMAN 1.763514326 DPTFIPAPIQAK_433.2_461.2 ANGT_HUMAN 1.760870392 VLEPTLK_400.3_458.3 VTDB_HUMAN 1.723389354 YENYTSSFFIR_713.8_756.4 IL12B_HUMAN 1.63355187

TABLE 43 Random Forest 100 Middle-Late Window Variable UniProt_ID MeanDecreaseGini VPLALFALNR_557.3_620.4 PEPD_HUMAN 1.995805024 VFQFLEK_455.8_811.4 CO5_HUMAN 1.235926416 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN 1.187464899 EVFSKPISWEELLQ_852.9_376.2 FA40A_HUMAN 1.166642578 AQPVQVAEGSEPDGFWEALGGK_758.0_574.3 GELS_HUMAN 1.146077071 TLAFVR_353.7_492.3 FA7_HUMAN 1.143038275 ANLINNIFELAGLGK_793.9_299.2 LCAP_HUMAN 1.130656591 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 1.098305298 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN 1.096715712 LLAPSDSPEWLSFDVTGVVR_730.1_430.3 TGFB1_HUMAN 1.086171713 YGIEEHGK_311.5_341.2 CXA1_HUMAN 1.071880823 ALNHLPLEYNSALYSR_621.0_696.4 CO6_HUMAN 1.062278869 TQILEWAAER_608.8_761.4 EGLN_HUMAN 1.059019017 AVYEAVLR_460.8_587.4 PEPD_HUMAN 1.057920661 AEIEYLEK_497.8_552.3 LYAM1_HUMAN 1.038388955 SEPRPGVLLR_375.2_654.4 FA7_HUMAN 1.028275728 AVDIPGLEAATPYR_736.9_399.2 TENA_HUMAN 1.026032369 LIEIANHVDK_384.6_498.3 ADA12_HUMAN 1.015065282 YGIEEHGK_311.5_599.3 CXA1_HUMAN 0.98667651 VLEPTLK_400.3_587.3 VTDB_HUMAN 0.970330675 DVLLLVHNLPQNLTGHIWYK_791.8_883.0 PSG7_HUMAN 0.934747674 TAHISGLPPSTDFIVYLSGLAPSIR_871.5_472.3 TENA_HUMAN 0.889111923 TLNAYDHR_330.5_312.2 PAR3_HUMAN 0.887605636 FGFGGSTDSGPIR_649.3_946.5 ADA12_HUMAN 0.884305889 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 0.880889836 SEYGAALAWEK_612.8_788.4 CO6_HUMAN 0.863585472 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 0.849232356 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 0.843334824 SEYGAALAWEK_612.8_845.5 CO6_HUMAN 0.842319271 TPSAAYLWVGTGASEAEK_919.5_849.4 GELS_HUMAN 0.828959173

TABLE 44 Random Forest Protein Middle-Late Window Variable UniProt_ID MeanDecreaseGini VPLALFALNR_557.3_620.4 PEPD_HUMAN 3.202123047 ANLINNIFELAGLGK_793.9_299.2 LCAP_HUMAN 2.100447309 VFQFLEK_455.8_811.4 CO5_HUMAN 2.096157529 AQPVQVAEGSEPDGFWEALGGK_758.0_574.3 GELS_HUMAN 2.052960939 ALNHLPLEYNSALYSR_621.0_696.4 CO6_HUMAN 2.046139797 TQILEWAAER_608.8_761.4 EGLN_HUMAN 1.99287941 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN 1.920894959 TGVAVNKPAEFTVDAK_549.6_258.1 FLNA_HUMAN 1.917665697 SEPRPGVLLR_375.2_654.4 FA7_HUMAN 1.883557705 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN 1.870232155 EVFSKPISWEELLQ_852.9_376.2 FA40A_HUMAN 1.869000136 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 1.825457092 VLEPTLK_400.3_587.3 VTDB_HUMAN 1.695327774 TEQAAVAR_423.2_615.4 FA12_HUMAN 1.685013152 LLAPSDSPEWLSFDVTGVVR_730.1_430.3 TGFB1_HUMAN 1.684068039 TLNAYDHR_330.5_312.2 PAR3_HUMAN 1.673758239 AVDIPGLEAATPYR_736.9_399.2 TENA_HUMAN 1.648896853 DVLLLVHNLPQNLTGHIWYK_791.8_883.0 PSG7_HUMAN 1.648146088 AEIEYLEK_497.8_552.3 LYAM1_HUMAN 1.645833005 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 1.639121965 AGLLRPDYALLGHR_518.0_595.4 PGRP2_HUMAN 1.610227875 YGIEEHGK_311.5_599.3 CXA1_HUMAN 1.606978339 QINSYVK_426.2_496.3 CBG_HUMAN 1.554905578 LTTVDIVTLR_565.8_815.5 IL2RB_HUMAN 1.484081016 AALAAFNAQNNGSNFQLEEISR_789.1_746.4 FETUA_HUMAN 1.43173022 AEVIWTSSDHQVLSGK_586.3_300.2 PD1L1_HUMAN 1.394857397 ALEQDLPVNIK_620.4_570.4 CNDP1_HUMAN 1.393464547 DFNQFSSGEK_386.8_333.2 FETA_HUMAN 1.374296237 TSYQVYSK_488.2_787.4 C163A_HUMAN 1.36141387 TLEAQLTPR_514.8_685.4 HEP2_HUMAN 1.311118611

TABLE 45 Random Forest All Middle-Late Window Variable UniProt_ID MeanDecreaseGini VPLALFALNR_557.3_620.4 PEPD_HUMAN 0.685165163 VFQFLEK_455.8_811.4 CO5_HUMAN 0.426827804 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 0.409942379 YGIEEHGK_311.5_341.2 CXA1_HUMAN 0.406589512 ALNHLPLEYNSALYSR_621.0_696.4 CO6_HUMAN 0.402152062 AQPVQVAEGSEPDGFWEALGGK_758.0_574.3 GELS_HUMAN 0.374861014 ANLINNIFELAGLGK_793.9_299.2 LCAP_HUMAN 0.367089422 TQILEWAAER_608.8_761.4 EGLN_HUMAN 0.353757524 AVYEAVLR_460.8_587.4 PEPD_HUMAN 0.350518668 TLAFVR_353.7_492.3 FA7_HUMAN 0.344669505 SEPRPGVLLR_375.2_654.4 FA7_HUMAN 0.338752336 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 0.321850027 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN 0.301819017 EVFSKPISWEELLQ_852.9_376.2 FA40A_HUMAN 0.299561811 LIEIANHVDK_384.6_498.3 ADA12_HUMAN 0.298253589 VLEPTLK_400.3_587.3 VTDB_HUMAN 0.296206088 YGIEEHGK_311.5_599.3 CXA1_HUMAN 0.295621408 DVLLLVHNLPQNLTGHIWYK_791.8_883.0 PSG7_HUMAN 0.292937475 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 0.275902848 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN 0.275664578 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 0.27120436 AVDIPGLEAATPYR_736.9_399.2 TENA_HUMAN 0.266568271 TGVAVNKPAEFTVDAK_549.6_258.1 FLNA_HUMAN 0.262537889 TLNAYDHR_330.5_312.2 PAR3_HUMAN 0.259901193 IYLQPGR_423.7_329.2 ITIH2_HUMAN 0.259086112 AEVIWTSSDHQVLSGK_586.3_300.2 PD1L1_HUMAN 0.25722354 VPSHAVVAR_312.5_515.3 TRFL_HUMAN 0.256151812 SEYGAALAWEK_612.8_845.5 CO6_HUMAN 0.251704855 FGFGGSTDSGPIR_649.3_946.5 ADA12_HUMAN 0.249400642 SEYGAALAWEK_612.8_788.4 CO6_HUMAN 0.245930393

TABLE 46 Random Forest 32 Late Window Variable UniProt_ID MeanDecreaseGini AVYEAVLR_460.8_587.4 PEPD_HUMAN 1.889521223 AEIEYLEK_497.8_552.3 LYAM1_HUMAN 1.75233545 AALAAFNAQNNGSNFQLEEISR_789.1_746.4 FETUA_HUMAN 1.676813493 TGVAVNKPAEFTVDAK_549.6_258.1 FLNA_HUMAN 1.600684153 AVYEAVLR_460.8_750.4 PEPD_HUMAN 1.462889662 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 1.364115361 VPLALFALNR_557.3_620.4 PEPD_HUMAN 1.324317148 QINSYVK_426.2_610.3 CBG_HUMAN 1.305932064 ITQDAQLK_458.8_702.4 CBG_HUMAN 1.263533228 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 1.245153376 LIEIANHVDK_384.6_498.3 ADA12_HUMAN 1.236529173 QINSYVK_426.2_496.3 CBG_HUMAN 1.221866266 YSHYNER_323.5_418.2 HABP2_HUMAN 1.169575572 YYGYTGAFR_549.3_450.3 TRFL_HUMAN 1.126684146 VGVISFAQK_474.8_580.3 TFR2_HUMAN 1.075283855 VFQYIDLHQDEFVQTLK_708.4_375.2 CNDP1_HUMAN 1.07279097 SPEAEDPLGVER_649.8_314.1 Z512B_HUMAN 1.05759256 DEIPHNDIALLK_459.9_510.8 HABP2_HUMAN 1.028933332 ALEQDLPVNIK_620.4_798.5 CNDP1_HUMAN 1.014443799 ALEQDLPVNIK_620.4_570.4 CNDP1_HUMAN 1.010573267 ILDGGNK_358.7_603.3 CXCL5_HUMAN 0.992175141 TSYQVYSK_488.2_787.4 C163A_HUMAN 0.95649585 YENYTSSFFIR_713.8_756.4 IL12B_HUMAN 0.955085198 SETEIHQGFQHLHQLFAK_717.4_447.2 CBG_HUMAN 0.944726739 TLPFSR_360.7_506.3 LYAM1_HUMAN 0.944426109 VLSSIEQK_452.3_691.4 1433S_HUMAN 0.933902495 AEIEYLEK_497.8_389.2 LYAM1_HUMAN 0.891235263 GTYLYNDCPGPGQDTDCR_697.0_666.3 TNR1A_HUMAN 0.87187037 SGVDLADSNQK_567.3_662.3 VGFR3_HUMAN 0.869821307 SGVDLADSNQK_567.3_591.3 VGFR3_HUMAN 0.839946466

TABLE 47 Random Forest 100 Late Window Variable UniProt_ID MeanDecreaseGini AVYEAVLR_460.8_587.4 PEPD_HUMAN 0.971695767 AEIEYLEK_497.8_552.3 LYAM1_HUMAN 0.920098693 TGVAVNKPAEFTVDAK_549.6_258.1 FLNA_HUMAN 0.786924487 AVYEAVLR_460.8_750.4 PEPD_HUMAN 0.772867983 AALAAFNAQNNGSNFQLEEISR_789.1_746.4 FETUA_HUMAN 0.744138513 AYSDLSR_406.2_375.2 SAMP_HUMAN 0.736078079 VPLALFALNR_557.3_620.4 PEPD_HUMAN 0.681784822 QINSYVK_426.2_610.3 CBG_HUMAN 0.585819307 LIEIANHVDK_384.6_498.3 ADA12_HUMAN 0.577161158 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 0.573055613 WSAGLTSSQVDLYIPK_883.0_515.3 CBG_HUMAN 0.569156128 ITQDAQLK_458.8_702.4 CBG_HUMAN 0.551017844 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 0.539330047 YYGYTGAFR_549.3_450.3 TRFL_HUMAN 0.527652175 VFQYIDLHQDEFVQTLK_708.4_375.2 CNDP1_HUMAN 0.484155289 FQLPGQK_409.2_429.2 PSG1_HUMAN 0.480394031 AVDIPGLEAATPYR_736.9_286.1 TENA_HUMAN 0.475252565 QINSYVK_426.2_496.3 CBG_HUMAN 0.4728541 YISPDQLADLYK_713.4_277.2 ENOA_HUMAN 0.470079977 TLPFSR_360.7_506.3 LYAM1_HUMAN 0.46881451 SPEAEDPLGVER_649.8_314.1 Z512B_HUMAN 0.4658941 ALEQDLPVNIK_620.4_798.5 CNDP1_HUMAN 0.463604174 YSHYNER_323.5_418.2 HABP2_HUMAN 0.453076307 VGVISFAQK_474.8_580.3 TFR2_HUMAN 0.437768219 LQDAGVYR_461.2_680.3 PD1L1_HUMAN 0.428524689 AEIEYLEK_497.8_389.2 LYAM1_HUMAN 0.42041448 TSYQVYSK_488.2_787.4 C163A_HUMAN 0.419411932 SVVLIPLGAVDDGEHSQNEK_703.0_798.4 CNDP1_HUMAN 0.415325735 ALEQDLPVNIK_620.4_570.4 CNDP1_HUMAN 0.407951733 ILDGGNK_358.7_603.3 CXCL5_HUMAN 0.401059572

TABLE 48 Random Forest Protein Late Window Variable UniProt_ID MeanDecreaseGini AVYEAVLR_460.8_587.4 PEPD_HUMAN 1.836010146 AEIEYLEK_497.8_552.3 LYAM1_HUMAN 1.739802548 AALAAFNAQNNGSNFQLEEISR_789.1_746.4 FETUA_HUMAN 1.455337749 TGVAVNKPAEFTVDAK_549.6_258.1 FLNA_HUMAN 1.395043941 AYSDLSR_406.2_375.2 SAMP_HUMAN 1.177349958 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 1.14243936 QINSYVK_426.2_496.3 CBG_HUMAN 1.05284482 ALEQDLPVNIK_620.4_798.5 CNDP1_HUMAN 0.971678206 YISPDQLADLYK_713.4_277.2 ENOA_HUMAN 0.902293734 AVDIPGLEAATPYR_736.9_286.1 TENA_HUMAN 0.893163413 SPEAEDPLGVER_649.8_314.1 Z512B_HUMAN 0.856551531 ILDGGNK_358.7_603.3 CXCL5_HUMAN 0.841485153 VGVISFAQK_474.8_580.3 TFR2_HUMAN 0.835256078 YYGYTGAFR_549.3_450.3 TRFL_HUMAN 0.831195917 YSHYNER_323.5_418.2 HABP2_HUMAN 0.814479968 FQLPGQK_409.2_276.1 PSG1_HUMAN 0.77635168 YENYTSSFFIR_713.8_756.4 IL12B_HUMAN 0.761241391 TEQAAVAR_423.2_615.4 FA12_HUMAN 0.73195592 SGVDLADSNQK_567.3_662.3 VGFR3_HUMAN 0.72504131 VLSSIEQK_452.3_691.4 1433S_HUMAN 0.713380314 GTYLYNDCPGPGQDTDCR_697.0_666.3 TNR1A_HUMAN 0.704248586 TSYQVYSK_488.2_787.4 C163A_HUMAN 0.69026345 TLEAQLTPR_514.8_685.4 HEP2_HUMAN 0.654641588 AEVIWTSSDHQVLSGK_586.3_300.2 PD1L1_HUMAN 0.634751081 TAVTANLDIR_537.3_288.2 CHL1_HUMAN 0.619871203 ITENDIQIALDDAK_779.9_632.3 APOB_HUMAN 0.606313398 TASDFITK_441.7_781.4 GELS_HUMAN 0.593535076 SPQAFYR_434.7_556.3 REL3_HUMAN 0.592004045 NHYTESISVAK_624.8_415.2 NEUR1_HUMAN 0.588383911 LTTVDIVTLR_565.8_815.5 IL2RB_HUMAN 0.587343951

TABLE 49 Random Forest All Late Window Variable UniProt_ID MeanDecreaseGini AVYEAVLR_460.8_587.4 PEPD_HUMAN 0.437300283 AEIEYLEK_497.8_552.3 LYAM1_HUMAN 0.371624293 AALAAFNAQNNGSNFQLEEISR_789.1_746.4 FETUA_HUMAN 0.304039734 TGVAVNKPAEFTVDAK_549.6_258.1 FLNA_HUMAN 0.280588526 AVYEAVLR_460.8_750.4 PEPD_HUMAN 0.266788699 AYSDLSR_406.2_375.2 SAMP_HUMAN 0.247412666 VPLALFALNR_557.3_620.4 PEPD_HUMAN 0.229955358 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 0.218186524 ITQDAQLK_458.8_702.4 CBG_HUMAN 0.217646659 WSAGLTSSQVDLYIPK_883.0_515.3 CBG_HUMAN 0.213840705 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 0.212794469 LIEIANHVDK_384.6_498.3 ADA12_HUMAN 0.208620264 QINSYVK_426.2_610.3 CBG_HUMAN 0.202054546 QINSYVK_426.2_496.3 CBG_HUMAN 0.197235139 FQLPGQK_409.2_429.2 PSG1_HUMAN 0.188311102 VFQYIDLHQDEFVQTLK_708.4_375.2 CNDP1_HUMAN 0.180534913 ALEQDLPVNIK_620.4_798.5 CNDP1_HUMAN 0.178464358 YYGYTGAFR_549.3_450.3 TRFL_HUMAN 0.176050092 ALFLDALGPPAVTR_720.9_640.4 INHA_HUMAN 0.171492975 FQLPGQK_409.2_276.1 PSG1_HUMAN 0.167576198 SETEIHQGFQHLHQLFAK_717.4_447.2 CBG_HUMAN 0.162231844 ALEQDLPVNIK_620.4_570.4 CNDP1_HUMAN 0.162165399 VPSHAVVAR_312.5_515.3 TRFL_HUMAN 0.156742065 AVDIPGLEAATPYR_736.9_286.1 TENA_HUMAN 0.153681405 FTFTLHLETPKPSISSSNLNPR_829.4_874.4 PSG1_HUMAN 0.152042057 VGVISFAQK_474.8_580.3 TFR2_HUMAN 0.149034355 TLPFSR_360.7_506.3 LYAM1_HUMAN 0.143223501 SLDFTELDVAAEK_719.4_874.5 ANGT_HUMAN 0.141216186 SPEAEDPLGVER_649.8_314.1 Z512B_HUMAN 0.139843479 YGIEEHGK_311.5_341.2 CXA1_HUMAN 0.135236953

TABLE 50 Selected Transitions for Early Window Transition Parent Protein LIQDAVTGLTVNGQITGDK_972.0_798.4 ITIH3_HUMAN VQTAHFK_277.5_431.2 CO8A_HUMAN FLNWIK_410.7_560.3 HABP2_HUMAN ITGFLKPGK_320.9_429.3 LBP_HUMAN ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN TYLHTYESEI_628.3_908.4 ENPP2_HUMAN LIENGYFHPVK_439.6_627.4 F13B_HUMAN AVLHIGEK_289.5_292.2 THBG_HUMAN QALEEFQK_496.8_680.3 CO8B_HUMAN TEFLSNYLTNVDDITLVPGTLGR_846.8_600.3 ENPP2_HUMAN TASDFITK_441.7_781.4 GELS_HUMAN LPNNVLQEK_527.8_844.5 AFAM_HUMAN AHYDLR_387.7_288.2 FETUA_HUMAN ITLPDFTGDLR_624.3_288.2 LBP_HUMAN IEGNLIFDPNNYLPK_874.0_414.2 APOB_HUMAN ITGFLKPGK_320.9_301.2 LBP_HUMAN FSVVYAK_407.2_381.2 FETUA_HUMAN ITGFLKPGK_320.9_429.3 LBP_HUMAN VFQFLEK_455.8_811.4 CO5_HUMAN LIQDAVTGLTVNGQITGDK_972.0_798.4 ITIH3_HUMAN DADPDTFFAK_563.8_825.4 AFAM_HUMAN

TABLE 51 Selected Proteins for Early Window Protein complement component C6 precursor CO6_HUMAN inter-alpha-trypsin inhibitor heavy chain H3 ITIH3_HUMAN preproprotein Coagulation factor XIII B chain F13B_HUMAN Ectonucleotide pyrophosphatase/phosphodiesterase ENPP2_HUMAN family member 2 Complement component C8 beta chain CO8B_HUMAN thyroxine-binding globulin precursor THBG_HUMAN Hyaluronan-binding protein 2 HABP2_HUMAN lipopolysaccharide-binding protein LBP_HUMAN Complement factor B CFAB_HUMAN Gelsolin GELS_HUMAN afamin precursor AFAM_HUMAN apolipoprotein B-100 precursor APOB_HUMAN complement component C5 CO5_HUMAN Alpha-2-HS-glycoprotein FETUA_HUMAN complement component C8 gamma chain CO8G_HUMAN

TABLE 52 Selected Transitions for Middle-Late Window Transition Patent Protein VPLALFALNR_557.3_620.4 PEPD_HUMAN VFQFLEK_455.8_811.4 CO5_HUMAN AQPVQVAEGSEPDGFWEALGGK_758.0_574.3 GELS_HUMAN LIEIANHVDK_384.6_498.3 ADA12_HUMAN TLAFVR_353.7_492.3 FA7_HUMAN ALNHLPLEYNSALYSR_621.0_696.4 CO6_HUMAN AVYEAVLR_460.8_587.4 PEPD_HUMAN SEPRPGVLLR_375.2_654.4 FA7_HUMAN TYLHTYESEI_628.3_515.3 ENPP2_HUMAN ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN

TABLE 53 Selected Proteins for Middle-Late Window Protein Xaa-Pro dipeptidase PEPD_HUMAN Leucyl-cystinyl aminopeptidase LCAP_HUMAN complement component C5 CO5_HUMAN Gelsolin GELS_HUMAN complement component C6 precursor CO6_HUMAN Endoglin precursor EGLN_HUMAN EGF-containing fibulin-like extracellular matrix FBLN3_HUMAN protein 1 coagulation factor VII isoform a FA7_HUMAN Disintegrin and metalloproteinase domain-containing ADA12_HUMAN protein 12 vitamin D-binding protein isoform 1 precursor VTDB_HUMAN coagulation factor XII precursor FA12_HUMAN Corticosteroid-binding globulin CBG_HUMAN

Example 6. Study V to Further Refine Preterm Birth Biomarkers

A additional hypothesis-dependent discovery study was performed with a further refined scheduled MRM assay. Less robust transitions were again removed to improve analytical performance and make room for the inclusion of stable-isotope labeled standards (SIS) corresponding to 79 analytes of interest identified in previous studies. SIS peptides have identical amino acid sequence, chromatographic and MS fragmentation behaviour as their endogenous peptide counterparts, but differ in mass. Therefore they can be used to reduce LC-MS analytical variability and confirm analyte identity. Samples included approximately 60 spontaneous PTB cases (delivery at less than 37 weeks, 0 days), and 180 term controls (delivery at greater than or equal to 37 weeks, 0 days). Each case was designated a “matched” control to within one day of blood draw and two “random” controls matched to the same 3 week blood draw window (17-19, 20-22 or 23-25 weeks gestation). For the purposes of analysis these three blood draw windows were combined. Samples were processed essentially as described previously, except that in this study, tryptic digests were reconstituted in a solution containing SIS standards. Raw analyte peak areas were Box-Cox transformed, corrected for run order and batch effects by regression and used for univariate and multivariate statistical analyses. Univariate analysis included determination of p-values for adjusted peak areas for all analytes from t-tests considering cases vs controls defined as either deliveries at >37 weeks (Table 54) or deliveries at >40 weeks (Table 55). Univariate analysis also included the determination of p-values for a linear model that evaluates the dependence of each analyte's adjusted peak area on the time to birth (gestational age at birth minus the gestational age at blood draw) (Table 56) and the gestational age at birth (Table 57). Additionally raw peak area ratios were calculated for endogenous analytes and their corresponding SIS counterparts, Box-Cox transformed and then used for univariate and multivariate statistical analyses. The above univariate analysis was repeated for analyte/SIS peak area ratio values, summarized in Tables 58-61, respectively.

Multivariate random forest regression models were built using analyte values and clinical variables (e.g. Maternal age, (MAGE), Body mass index, (BMI)) to predict Gestational Age at Birth (GAB). The accuracy of the random forest was evaluated with respect to correlation of the predicted and actual GAB, and with respect to the mean absolute deviation (MAD) of the predicted from actual GAB. The accuracy was further evaluated by determining the area under the receiver operating characteristic curve (AUC) when using the predicted GAB as a quantitative variable to classify subjects as full term or pre-term. Random Forest Importance Values were fit to an Empirical Cumulative Distribution Function and probabilities (P) were calculated. We report the analytes by importance ranking (P>0.7) in the random forest models, using adjusted analyte peak area values (Table 62) and analyte/SIS peak area ratio values (Table 63).

The probability of pre-term birth, p(PTB), may be estimated using the predicted gestational age at birth (GAB) as follows. The estimate will be based on women enrolled in the Sera PAPR clinical trial, which provided the subjects used to develop the PTB prediction methods.

Among women with a predicted GAB of j days plus or minus k days, p(PTB) was estimated as the proportion of women in the PAPR clinical trial with a predicted GAB of j days plus or minus k days who actually deliver before 37 weeks gestational age.

More generally, for women with a predicted GAB of j days plus or minus k days, the probability that the actual gestational age at birth will be less than a specified gestational age, p(actual GAB<specified GAB), was estimated as the proportion of women in the PAPR clinical trial with a predicted GAB of j days plus or minus k days who actually deliver before the specified gestational age. FIG. 1 depicts a scatterplot of actual gestational age at birth versus predicted gestational age from random forest regression model. FIG. 2 shows the distribution of predicted gestational age from random forest regression model versus actual gestational age at birth (GAB), where actual GAB was given in categories of (i) less than 37 weeks, (ii) 37 to 39 weeks, and (iii) 40 weeks or greater.

TABLE 54 Univariate p-values for Adjusted Peak Areas (<37 vs >37 weeks) Transition Protein pvalue SPELQAEAK_486.8_659.4 APOA2_HUMAN 0.00246566 ALALPPLGLAPLLNLWAKPQGR_770.5_457.3 SHBG_HUMAN 0.002623332 ALALPPLGLAPLLNLWAKPQGR_770.5_256.2 SHBG_HUMAN 0.002822593 SPELQAEAK_486.8_788.4 APOA2_HUMAN 0.003183869 VVLSSGSGPGLDLPLVLGLPLQLK_791.5_768.5 SHBG_HUMAN 0.004936049 VVLSSGSGPGLDLPLVLGLPLQLK_791.5_598.4 SHBG_HUMAN 0.005598977 DYWSTVK_449.7_347.2 APOC3_HUMAN 0.005680405 DYWSTVK_449.7_620.3 APOC3_HUMAN 0.006288693 WGAAPYR_410.7_634.3 PGRP2_HUMAN 0.006505238 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN 0.007626246 DALSSVQESQVAQQAR_573.0_672.4 APOC3_HUMAN 0.008149335 LSIPQITTK_500.8_687.4 PSG5_HUMAN 0.009943955 GWVTDGFSSLK_598.8_854.4 APOC3_HUMAN 0.010175055 IALGGLLFPASNLR_481.3_657.4 SHBG_HUMAN 0.010784167 AKPALEDLR_506.8_813.5 APOA1_HUMAN 0.011331968 WGAAPYR_410.7_577.3 PGRP2_HUMAN 0.011761088 VPLALFALNR_557.3_620.4 PEPD_HUMAN 0.014050395 FSLVSGWGQLLDR_493.3_447.3 FA7_HUMAN 0.014271151 LSIPQITTK_500.8_800.5 PSG5_HUMAN 0.014339942 TLAFVR_353.7_274.2 FA7_HUMAN 0.014459876 DVLLLVHNLPQNLPGYFWYK_810.4_960.5 PSG9_HUMAN 0.016720007 FSVVYAK_407.2_381.2 FETUA_HUMAN 0.016792786 DVLLLVHNLPQNLPGYFWYK_810.4_215.1 PSG9_HUMAN 0.017335929 SEPRPGVLLR_375.2_654.4 FA7_HUMAN 0.018147773 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 0.019056484 WNFAYWAAHQPWSR_607.3_545.3 PRG2_HUMAN 0.019190043 ALNHLPLEYNSALYSR_621.0_696.4 CO6_HUMAN 0.020218682 AQPVQVAEGSEPDGFWEALGGK_758.0_623.4 GELS_HUMAN 0.020226218 GWVTDGFSSLK_598.8_953.5 APOC3_HUMAN 0.023192703 IALGGLLFPASNLR_481.3_412.3 SHBG_HUMAN 0.023916911 WNFAYWAAHQPWSR_607.3_673.3 PRG2_HUMAN 0.026026975 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 0.027731407 SEYGAALAWEK_612.8_788.4 CO6_HUMAN 0.031865281 DADPDTFFAK_563.8_302.1 AFAM_HUMAN 0.0335897 LFIPQITR_494.3_614.4 PSG9_HUMAN 0.034140767 DVLLLVHNLPQNLPGYFWYK_810.4_328.2 PSG9_HUMAN 0.034653304 TLAFVR_353.7_492.3 FA7_HUMAN 0.036441189 AVLHIGEK_289.5_292.2 THBG_HUMAN 0.038539433 IHPSYTNYR_384.2_452.2 PSG2_HUMAN 0.039733019 AGLLRPDYALLGHR_518.0_369.2 PGRP2_HUMAN 0.040916226 ILILPSVTR_506.3_559.3 PSGx_HUMAN 0.042460036 YYLQGAK_421.7_516.3 ITIH4_HUMAN 0.044511962 TPSAAYLWVGTGASEAEK_919.5_849.4 GELS_HUMAN 0.046362381 AGLLRPDYALLGHR_518.0_595.4 PGRP2_HUMAN 0.046572355 TYLHTYESEI_628.3_908.4 ENPP2_HUMAN 0.04754503 FSLVSGWGQLLDR_493.3_403.2 FA7_HUMAN 0.048642964 VNFTEIQK_489.8_765.4 FETA_HUMAN 0.04871392 LFIPQITR_494.3_727.4 PSG9_HUMAN 0.049288923 DISEVVTPR_508.3_787.4 CFAB_HUMAN 0.049458374 SEPRPGVLLR_375.2_454.3 FA7_HUMAN 0.049567047

TABLE 55 Univariate p-values for Adjusted Peak Areas (<37 vs >40 weeks) Transition Protein pvalue SPELQAEAK_486.8_659.4 APOA2_HUMAN 0.001457796 DYWSTVK_449.7_347.2 APOC3_HUMAN 0.001619622 DYWSTVK_449.7_620.3 APOC3_HUMAN 0.002068704 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN 0.00250563 GWVTDGFSSLK_598.8_854.4 APOC3_HUMAN 0.002543943 SPELQAEAK_486.8_788.4 APOA2_HUMAN 0.003108814 SEPRPGVLLR_375.2_654.4 FA7_HUMAN 0.004035832 DALSSVQESQVAQQAR_573.0_672.4 APOC3_HUMAN 0.00434652 SEYGAALAWEK_612.8_788.4 CO6_HUMAN 0.005306924 GWVTDGFSSLK_598.8_953.5 APOC3_HUMAN 0.005685534 ALNHLPLEYNSALYSR_621.0_696.4 CO6_HUMAN 0.005770384 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 0.005798991 ENPAVIDFELAPIVDLVR_670.7_601.4 CO6_HUMAN 0.006248095 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 0.006735817 TYLHTYESEI_628.3_908.4 ENPP2_HUMAN 0.007351774 AGLLRPDYALLGHR_518.0_369.2 PGRP2_HUMAN 0.009541521 AKPALEDLR_506.8_813.5 APOA1_HUMAN 0.009780371 SEYGAALAWEK_612.8_845.5 CO6_HUMAN 0.010085363 FSLVSGWGQLLDR_493.3_447.3 FA7_HUMAN 0.010401836 WGAAPYR_410.7_634.3 PGRP2_HUMAN 0.011233623 ENPAVIDFELAPIVDLVR_670.7_811.5 CO6_HUMAN 0.012029564 DVLLLVHNLPQNLPGYFWYK_810.4_215.1 PSG9_HUMAN 0.014808277 LFIPQITR_494.3_614.4 PSG9_HUMAN 0.015879755 WGAAPYR_410.7_577.3 PGRP2_HUMAN 0.016562435 AGLLRPDYALLGHR_518.0_595.4 PGRP2_HUMAN 0.016793521 TLAFVR_353.7_492.3 FA7_HUMAN 0.016919708 FSLVSGWGQLLDR_493.3_403.2 FA7_HUMAN 0.016937583 WWGGQPLWITATK_772.4_373.2 ENPP2_HUMAN 0.019050115 GYVIIKPLVWV_643.9_304.2 SAMP_HUMAN 0.019675317 DVLLLVHNLPQNLPGYFWYK_810.4_960.5 PSG9_HUMAN 0.020387647 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 0.020458335 DVLLLVHNLPQNLPGYFWYK_810.4_328.2 PSG9_HUMAN 0.021488084 WWGGQPLWITATK_772.4_929.5 ENPP2_HUMAN 0.021709354 LDFHFSSDR_375.2_448.2 INHBC_HUMAN 0.022403383 LFIPQITR_494.3_727.4 PSG9_HUMAN 0.025561103 TEFLSNYLTNVDDITLVPGTLGR_846.8_600.3 ENPP2_HUMAN 0.029344366 LSIPQITTK_500.8_800.5 PSG5_HUMAN 0.031361776 ALVLELAK_428.8_672.4 INHBE_HUMAN 0.031690737 SEPRPGVLLR_375.2_454.3 FA7_HUMAN 0.033067953 LSIPQITTK_500.8_687.4 PSG5_HUMAN 0.033972449 LDFHFSSDR_375.2_611.3 INHBC_HUMAN 0.034500249 LDFHFSSDR_375.2_464.2 INHBC_HUMAN 0.035166664 GAVHVVVAETDYQSFAVLYLER_822.8_580.3 CO8G_HUMAN 0.037334975 HELTDEELQSLFTNFANVVDK_817.1_854.4 AFAM_HUMAN 0.039258528 AYSDLSR_406.2_375.2 SAMP_HUMAN 0.04036485 YYLQGAK_421.7_516.3 ITIH4_HUMAN 0.042204165 ILPSVPK_377.2_264.2 PGH1_HUMAN 0.042397885 ELLESYIDGR_597.8_710.4 THRB_HUMAN 0.043053589 ALALPPLGLAPLLNLWAKPQGR_770.5_256.2 SHBG_HUMAN 0.045692283 VGEYSLYIGR_578.8_871.5 SAMP_HUMAN 0.04765767 ANDQYLTAAALHNLDEAVK_686.4_317.2 IL1A_HUMAN 0.048928376 YYGYTGAFR_549.3_551.3 TRFL_HUMAN 0.049568351

TABLE 56 Univariate p-values for Adjusted Peak Areas in Time to Birth Linear Model Protein pvalue ADA12_HUMAN 0.003412707 ENPP2_HUMAN 0.003767393 ADA12_HUMAN 0.004194234 ENPP2_HUMAN 0.004298493 ADA12_HUMAN 0.004627197 ADA12_HUMAN 0.004918852 ENPP2_HUMAN 0.005792374 CO6_HUMAN 0.005858282 ENPP2_HUMAN 0.007123606 CO6_HUMAN 0.007162317 ENPP2_HUMAN 0.008228726 ENPP2_HUMAN 0.009168492 PSG9_HUMAN 0.011531192 PSG9_HUMAN 0.019389627 PSG9_HUMAN 0.023680865 INHBE_HUMAN 0.02581564 B2MG_HUMAN 0.026544689 LBP_HUMAN 0.031068274 PSG9_HUMAN 0.031091843 APOA2_HUMAN 0.033130498 INHBC_HUMAN 0.03395215 CBG_HUMAN 0.034710348 PSGx_HUMAN 0.035719227 CBG_HUMAN 0.036331871 CSH_HUMAN 0.039896611 CSH_HUMAN 0.04244001 SAMP_HUMAN 0.047112128 LBP_HUMAN 0.048141371 LBP_HUMAN 0.048433174 CO6_HUMAN 0.04850949 PSGx_HUMAN 0.049640167

TABLE 57 Univariate p-values for Adjusted Peak Areas in Gestation Age at Birth Linear Model Transition Protein pvalue ENPAVIDFELAPIVDLVR_670.7_811.5 CO6_HUMAN 0.000117239 ENPAVIDFELAPIVDLVR_670.7_601.4 CO6_HUMAN 0.000130113 TYLHTYESEI_628.3_908.4 ENPP2_HUMAN 0.000160472 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 0.000175167 TEFLSNYLTNVDDITLVPGTLGR_846.8_600.3 ENPP2_HUMAN 0.000219886 TEFLSNYLTNVDDITLVPGTLGR_846.8_699.4 ENPP2_HUMAN 0.000328416 WWGGQPLWITATK_772.4_373.2 ENPP2_HUMAN 0.000354644 WWGGQPLWITATK_772.4_929.5 ENPP2_HUMAN 0.000390821 SEYGAALAWEK_612.8_788.4 CO6_HUMAN 0.000511882 LDFHFSSDR_375.2_448.2 INHBC_HUMAN 0.000600637 ALVLELAK_428.8_672.4 INHBE_HUMAN 0.000732445 GLQYAAQEGLLALQSELLR_1037.1_929.5 LBP_HUMAN 0.000743924 DVLLLVHNLPQNLPGYFWYK_810.4_960.5 PSG9_HUMAN 0.000759173 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 0.001224347 DVLLLVHNLPQNLPGYFWYK_810.4_328.2 PSG9_HUMAN 0.001241329 GYVIIKPLVWV_643.9_304.2 SAMP_HUMAN 0.001853785 SPELQAEAK_486.8_659.4 APOA2_HUMAN 0.001856303 GLQYAAQEGLLALQSELLR_1037.1_858.5 LBP_HUMAN 0.001978165 LDFHFSSDR_375.2_611.3 INHBC_HUMAN 0.002098948 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 0.002212096 SFRPFVPR_335.9_272.2 LBP_HUMAN 0.002545286 SFRPFVPR_335.9_635.3 LBP_HUMAN 0.002620268 WSAGLTSSQVDLYIPK_883.0_515.3 CBG_HUMAN 0.002787272 DLHLSDVFLK_396.2_260.2 CO6_HUMAN 0.002954612 LIEIANHVDK_384.6_498.3 ADA12_HUMAN 0.002955081 DVLLLVHNLPQNLPGYFWYK_810.4_215.1 PSG9_HUMAN 0.003541011 LFIPQITR_494.3_614.4 PSG9_HUMAN 0.003750666 FGFGGSTDSGPIR_649.3_946.5 ADA12_HUMAN 0.003773696 YYLQGAK_421.7_516.3 ITIH4_HUMAN 0.004064026 SEYGAALAWEK_612.8_845.5 CO6_HUMAN 0.004208136 AITPPHPASQANIIFDITEGNLR_825.8_459.3 FBLN1_HUMAN 0.004709104 LDFHFSSDR_375.2_464.2 INHBC_HUMAN 0.005355741 HELTDEELQSLFTNFANVVDK_817.1_854.4 AFAM_HUMAN 0.005370567 ALNHLPLEYNSALYSR_621.0_696.4 CO6_HUMAN 0.005705922 ITQDAQLK_458.8_702.4 CBG_HUMAN 0.006762484 ITLPDFTGDLR_624.3_920.5 LBP_HUMAN 0.006993268 SILFLGK_389.2_577.4 THBG_HUMAN 0.007134146 WSAGLTSSQVDLYIPK_883.0_357.2 CBG_HUMAN 0.007670388 GVTSVSQIFHSPDLAIR_609.7_472.3 IC1_HUMAN 0.007742729 VGEYSLYIGR_578.8_871.5 SAMP_HUMAN 0.007778691 ITLPDFTGDLR_624.3_288.2 LBP_HUMAN 0.008179918 YYLQGAK_421.7_327.1 ITIH4_HUMAN 0.008404686 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 0.008601162 DYWSTVK_449.7_620.3 APOC3_HUMAN 0.008626786 TVQAVLTVPK_528.3_855.5 PEDF_HUMAN 0.008907523 ITGFLKPGK_320.9_301.2 LBP_HUMAN 0.009155417 LFIPQITR_494.3_727.4 PSG9_HUMAN 0.009571006 SPELQAEAK_486.8_788.4 APOA2_HUMAN 0.009776508 DYWSTVK_449.7_347.2 APOC3_HUMAN 0.00998356 ITGFLKPGK_320.9_429.3 LBP_HUMAN 0.010050264 FLNWIK_410.7_560.3 HABP2_HUMAN 0.010372454 DLHLSDVFLK_396.2_366.2 CO6_HUMAN 0.010806378 GVTSVSQIFHSPDLAIR_609.7_908.5 IC1_HUMAN 0.011035991 VEHSDLSFSK_383.5_468.2 B2MG_HUMAN 0.011113172 LLDSLPSDTR_558.8_276.2 IC1_HUMAN 0.011589013 LLDSLPSDTR_558.8_890.4 IC1_HUMAN 0.011629438 QALEEFQK_496.8_551.3 CO8B_HUMAN 0.011693839 LLDSLPSDTR_558.8_575.3 IC1_HUMAN 0.012159314 IIGGSDADIK_494.8_762.4 C1S_HUMAN 0.013080243 AFIQLWAFDAVK_704.9_650.4 AMBP_HUMAN 0.013462234 GFQALGDAADIR_617.3_717.4 TIMP1_HUMAN 0.014370997 LPNNVLQEK_527.8_730.4 AFAM_HUMAN 0.014424891 DTDTGALLFIGK_625.8_217.1 PEDF_HUMAN 0.014967952 VQTAHFK_277.5_502.3 CO8A_HUMAN 0.01524844 ILILPSVTR_506.3_559.3 PSGx_HUMAN 0.015263132 SILFLGK_389.2_201.1 THBG_HUMAN 0.015265233 TVQAVLTVPK_528.3_428.3 PEDF_HUMAN 0.015344052 VEPLYELVTATDFAYSSTVR_754.4_712.4 CO8B_HUMAN 0.015451068 FSLVSGWGQLLDR_493.3_447.3 FA7_HUMAN 0.015510454 GWVTDGFSSLK_598.8_854.4 APOC3_HUMAN 0.01610797 LSETNR_360.2_519.3 PSG1_HUMAN 0.016433362 TQILEWAAER_608.8_632.3 EGLN_HUMAN 0.01644844 SETEIHQGFQHLHQLFAK_717.4_318.1 CBG_HUMAN 0.016720367 TNLESILSYPK_632.8_936.5 IC1_HUMAN 0.017314185 TNLESILSYPK_632.8_807.5 IC1_HUMAN 0.017593786 AYSDLSR_406.2_375.2 SAMP_HUMAN 0.018531348 YEVQGEVFTKPQLWP_911.0_392.2 CRP_HUMAN 0.019111323 AYSDLSR_406.2_577.3 SAMP_HUMAN 0.019271266 QALEEFQK_496.8_680.3 CO8B_HUMAN 0.019429489 APLTKPLK_289.9_398.8 CRP_HUMAN 0.020110081 FQPTLLTLPR_593.4_276.1 IC1_HUMAN 0.020114306 ITQDAQLK_458.8_803.4 CBG_HUMAN 0.020401782 AVLHIGEK_289.5_292.2 THBG_HUMAN 0.02056597 ANDQYLTAAALHNLDEAVK_686.4_317.2 IL1A_HUMAN 0.020770124 VGEYSLYIGR_578.8_708.4 SAMP_HUMAN 0.021126414 TLYSSSPR_455.7_533.3 IC1_HUMAN 0.021306106 VEHSDLSFSK_383.5_234.1 B2MG_HUMAN 0.021640643 HELTDEELQSLFTNFANVVDK_817.1_906.5 AFAM_HUMAN 0.021921609 TLYSSSPR_455.7_696.3 IC1_HUMAN 0.022196181 GYVIIKPLVWV_643.9_854.6 SAMP_HUMAN 0.023126336 DEIPHNDIALLK_459.9_260.2 HABP2_HUMAN 0.023232158 ILILPSVTR_506.3_785.5 PSGx_HUMAN 0.023519909 WNFAYWAAHQPWSR_607.3_545.3 PRG2_HUMAN 0.023697087 FQPTLLTLPR_593.4_712.5 IC1_HUMAN 0.023751959 AQPVQVAEGSEPDGFWEALGGK_758.0_623.4 GELS_HUMAN 0.024262721 DEIPHNDIALLK_459.9_510.8 HABP2_HUMAN 0.024414348 GDSGGAFAVQDPNDK_739.3_716.3 C1S_HUMAN 0.025075028 FLNWIK_410.7_561.3 HABP2_HUMAN 0.025649617 APLTKPLK_289.9_357.2 CRP_HUMAN 0.025961162 ALDLSLK_380.2_185.1 ITIH3_HUMAN 0.026233504 GWVTDGFSSLK_598.8_953.5 APOC3_HUMAN 0.026291884 SETEIHQGFQHLHQLFAK_717.4_447.2 CBG_HUMAN 0.026457136 GDSGGAFAVQDPNDK_739.3_473.2 C1S_HUMAN 0.02727457 YEVQGEVFTKPQLWP_911.0_293.1 CRP_HUMAN 0.028244448 HVVQLR_376.2_614.4 IL6RA_HUMAN 0.028428028 DTDTGALLFIGK_625.8_818.5 PEDF_HUMAN 0.028773557 EVPLSALTNILSAQLISHWK_740.8_996.6 PAI1_HUMAN 0.029150774 AFTECCVVASQLR_770.9_574.3 CO5_HUMAN 0.029993325 TLAFVR_353.7_492.3 FA7_HUMAN 0.030064307 LWAYLTIQELLAK_781.5_300.2 ITIH1_HUMAN 0.030368674 DEIPHNDIALLK_459.9_245.1 HABP2_HUMAN 0.031972082 AGLLRPDYALLGHR_518.0_369.2 PGRP2_HUMAN 0.032057409 AVYEAVLR_460.8_587.4 PEPD_HUMAN 0.032527521 LPNNVLQEK_527.8_844.5 AFAM_HUMAN 0.033807082 GAVHVVVAETDYQSFAVLYLER_822.8_580.3 CO8G_HUMAN 0.034370139 WNFAYWAAHQPWSR_607.3_673.3 PRG2_HUMAN 0.0349737 EAQLPVIENK_570.8_329.2 PLMN_HUMAN 0.035304322 VQEAHLTEDQIFYFPK_655.7_701.4 CO8G_HUMAN 0.035704382 AFIQLWAFDAVK_704.9_836.4 AMBP_HUMAN 0.035914532 SGFSFGFK_438.7_585.3 CO8B_HUMAN 0.037168221 SGFSFGFK_438.7_732.4 CO8B_HUMAN 0.040182596 DADPDTFFAK_563.8_302.1 AFAM_HUMAN 0.041439744 EAQLPVIENK_570.8_699.4 PLMN_HUMAN 0.041447675 IIGGSDADIK_494.8_260.2 C1S_HUMAN 0.041683256 AVLTIDEK_444.8_718.4 A1AT_HUMAN 0.043221658 SEPRPGVLLR_375.2_654.4 FA7_HUMAN 0.044079127 YHFEALADTGISSEFYDNANDLLSK_940.8_874.5 CO8A_HUMAN 0.045313634 HFQNLGK_422.2_527.2 AFAM_HUMAN 0.047118971 LEQGENVFLQATDK_796.4_822.4 C1QB_HUMAN 0.047818928 NTVISVNPSTK_580.3_732.4 VCAM1_HUMAN 0.048102262 YYGYTGAFR_549.3_551.3 TRFL_HUMAN 0.048331316 ISLLLIESWLEPVR_834.5_500.3 CSH_HUMAN 0.049561581 LQVLGK_329.2_416.3 A2GL_HUMAN 0.049738493

TABLE 58 Univariate p-values for Peak Area Ratios (<37 vs >37 weeks) UniProt_ID Transition pvalue SHBG_HUMAN IALGGLLFPASNLR_481.3_657.4 0.006134652 SHBG_HUMAN IALGGLLFPASNLR_481.3_412.3 0.019049498 APOC3_HUMAN DALSSVQESQVAQQAR_573.0_672.4 0.020688543 THBG_HUMAN AVLHIGEK_289.5_292.2 0.0291698 PSG9_HUMAN DVLLLVHNLPQNLPGYFWYK_810.4_960.5 0.033518454 APOC3_HUMAN DALSSVQESQVAQQAR_573.0_502.3 0.043103265 PSG9_HUMAN LFIPQITR_494.3_614.4 0.04655948

TABLE 59 Univariate p-values for Peak Area Ratios (<37 vs >40 weeks) UniProt_ID Transition pvalue APOC3_HUMAN DALSSVQESQVAQQAR_573.0_672.4 0.011174438 APOC3_HUMAN DALSSVQESQVAQQAR_573.0_502.3 0.015231617 PSG9_HUMAN LFIPQITR_494.3_614.4 0.018308413 PSG9_HUMAN LFIPQITR_494.3_727.4 0.027616871 PSG9_HUMAN DVLLLVHNLPQNLPGYFWYK_810.4_960.5 0.028117582 THBG_HUMAN AVLHIGEK_289.5_292.2 0.038899107 CO6_HUMAN ALNHLPLEYNSALYSR_621.0_696.4 0.040662269 ENPP2_HUMAN TYLHTYESEI_628.3_908.4 0.044545826

TABLE 60 Univariate p-values for Peak Area Ratios in Time to Birth Linear Model UniProt_ID Transition pvalue ADA12_HUMAN FGFGGSTDSGPIR_649.3_946.5 5.85E−27 ADA12_HUMAN FGFGGSTDSGPIR_649.3_745.4 2.65E−24 PSG4_HUMAN TLFIFGVTK_513.3_215.1 1.07E−20 PSG4_HUMAN TLFIFGVTK_513.3_811.5 2.32E−20 PSGx_HUMAN ILILPSVTR_506.3_785.5 8.25E−16 PSGx_HUMAN ILILPSVTR_506.3_559.3 9.72E−16 PSG1_HUMAN FQLPGQK_409.2_429.2 1.29E−12 PSG11_HUMAN LFIPQITPK_528.8_261.2 2.11E−12 PSG1_HUMAN FQLPGQK_409.2_276.1 2.33E−12 PSG11_HUMAN LFIPQITPK_528.8_683.4 3.90E−12 PSG6_HUMAN SNPVTLNVLYGPDLPR_585.7_817.4 5.71E−12 PSG6_HUMAN SNPVTLNVLYGPDLPR_585.7_654.4 1.82E−11 VGFR3_HUMAN SGVDLADSNQK_567.3_662.3 4.57E−11 INHBE_HUMAN ALVLELAK_428.8_331.2 1.04E−08 PSG2_HUMAN IHPSYTNYR_384.2_452.2 6.27E−08 PSG9_HUMAN LFIPQITR_494.3_727.4 1.50E−07 VGFR3_HUMAN SGVDLADSNQK_567.3_591.3 2.09E−07 PSG9_HUMAN LFIPQITR_494.3_614.4 2.71E−07 PSG9_HUMAN DVLLLVHNLPQNLPGYFWYK_810.4_960.5 3.10E−07 PSG2_HUMAN IHPSYTNYR_384.2_338.2 2.55E−06 ITIH3_HUMAN LIQDAVTGLTVNGQITGDK_972.0_640.4 2.76E−06 ENPP2_HUMAN TYLHTYESEI_628.3_908.4 2.82E−06 ENPP2_HUMAN WWGGQPLWITATK_772.4_373.2 3.75E−06 PSG9_HUMAN DVLLLVHNLPQNLPGYFWYK_810.4_328.2 3.94E−06 B2MG_HUMAN VEHSDLSFSK_383.5_468.2 5.42E−06 ENPP2_HUMAN WWGGQPLWITATK_772.4_929.5 7.93E−06 ANGT_HUMAN ALQDQLVLVAAK_634.9_289.2 1.04E−05 B2MG_HUMAN VNHVTLSQPK_374.9_244.2 1.46E−05 AFAM_HUMAN LPNNVLQEK_527.8_730.4 1.50E−05 AFAM_HUMAN LPNNVLQEK_527.8_844.5 1.98E−05 THBG_HUMAN AVLHIGEK_289.5_292.2 2.15E−05 ENPP2_HUMAN TYLHTYESEI_628.3_515.3 2.17E−05 IL12B_HUMAN DIIKPDPPK_511.8_342.2 3.31E−05 AFAM_HUMAN DADPDTFFAK_563.8_302.1 6.16E−05 THBG_HUMAN AVLHIGEK_289.5_348.7 8.34E−05 PSG9_HUMAN DVLLLVHNLPQNLPGYFWYK_810.4_215.1 0.000104442 B2MG_HUMAN VEHSDLSFSK_383.5_234.1 0.000140786 TRFL_HUMAN YYGYTGAFR_549.3_450.3 0.000156543 HEMO_HUMAN QGHNSVFLIK_381.6_260.2 0.000164578 A1BG_HUMAN LLELTGPK_435.8_227.2 0.000171113 CO6_HUMAN ALNHLPLEYNSALYSR_621.0_696.4 0.000242116 CO6_HUMAN ALNHLPLEYNSALYSR_621.0_538.3 0.00024681 ALS_HUMAN IRPHTFTGLSGLR_485.6_432.3 0.000314359 ITIH2_HUMAN LSNENHGIAQR_413.5_544.3 0.0004877 PEDF_HUMAN TVQAVLTVPK_528.3_855.5 0.000508174 AFAM_HUMAN HFQNLGK_422.2_527.2 0.000522139 FLNA_HUMAN TGVAVNKPAEFTVDAK_549.6_258.1 0.000594403 ANGT_HUMAN ALQDQLVLVAAK_634.9_956.6 0.000640673 AFAM_HUMAN HFQNLGK_422.2_285.1 0.000718763 HGFA_HUMAN LHKPGVYTR_357.5_692.4 0.000753293 HGFA_HUMAN LHKPGVYTR_357.5_479.3 0.000909298 HABP2_HUMAN FLNWIK_410.7_561.3 0.001282014 FETUA_HUMAN HTLNQIDEVK_598.8_951.5 0.001389792 AFAM_HUMAN DADPDTFFAK_563.8_825.4 0.001498237 B2MG_HUMAN VNHVTLSQPK_374.9_459.3 0.001559862 ALS_HUMAN IRPHTFTGLSGLR_485.6_545.3 0.001612361 A1BG_HUMAN LLELTGPK_435.8_644.4 0.002012656 F13B_HUMAN LIENGYFHPVK_439.6_343.2 0.00275216 ITIH2_HUMAN LSNENHGIAQR_413.5_519.8 0.00356561 APOC3_HUMAN DALSSVQESQVAQQAR_573.0_672.4 0.00392745 F13B_HUMAN LIENGYFHPVK_439.6_627.4 0.00434836 PEDF_HUMAN TVQAVLTVPK_528.3_428.3 0.00482765 PLMN_HUMAN YEFLNGR_449.7_293.1 0.007325436 HEMO_HUMAN QGHNSVFLIK_381.6_520.4 0.009508516 FETUA_HUMAN HTLNQIDEVK_598.8_958.5 0.010018936 CO5_HUMAN LQGTLPVEAR_542.3_842.5 0.011140661 PLMN_HUMAN YEFLNGR_449.7_606.3 0.01135322 CO5_HUMAN TLLPVSKPEIR_418.3_288.2 0.015045275 HABP2_HUMAN FLNWIK_410.7_560.3 0.01523134 APOC3_HUMAN DALSSVQESQVAQQAR_573.0_502.3 0.01584708 CO5_HUMAN LQGTLPVEAR_542.3_571.3 0.017298064 CFAB_HUMAN DISEVVTPR_508.3_472.3 0.021743221 CERU_HUMAN TTIEKPVWLGFLGPIIK_638.0_640.4 0.02376225 CO8G_HUMAN SLPVSDSVLSGFEQR_810.9_723.3 0.041150397 CO8G_HUMAN FLQEQGHR_338.8_497.3 0.042038143 CO5_HUMAN VFQFLEK_455.8_811.4 0.043651929 CO8B_HUMAN QALEEFQK_496.8_680.3 0.04761631

TABLE 61 Univariate p-values for Peak Area Ratios in Gestation Age at Birth Linear Model UniProt_ID Transition pvalue PSG9_HUMAN DVLLLVHNLPQNLPGYFWYK_810.4_960.5 0.000431547 B2MG_HUMAN VEHSDLSFSK_383.5_468.2 0.000561148 PSG9_HUMAN DVLLLVHNLPQNLPGYFWYK_810.4_328.2 0.000957509 ENPP2_HUMAN TYLHTYESEI_628.3_908.4 0.001058809 THBG_HUMAN AVLHIGEK_289.5_292.2 0.001180484 ENPP2_HUMAN WWGGQPLWITATK_772.4_373.2 0.001524983 PSG9_HUMAN LFIPQITR_494.3_614.4 0.001542932 ENPP2_HUMAN WWGGQPLWITATK_772.4_929.5 0.002047607 ENPP2_HUMAN TYLHTYESEI_628.3_515.3 0.003087492 PSG9_HUMAN LFIPQITR_494.3_727.4 0.00477154 PSG9_HUMAN DVLLLVHNLPQNLPGYFWYK_810.4_215.1 0.004824351 THBG_HUMAN AVLHIGEK_289.5_348.7 0.006668084 AFAM_HUMAN LPNNVLQEK_527.8_730.4 0.006877647 ADA12_HUMAN FGFGGSTDSGPIR_649.3_745.4 0.011738104 PEDF_HUMAN TVQAVLTVPK_528.3_855.5 0.013349511 A1BG_HUMAN LLELTGPK_435.8_227.2 0.015793885 ITIH3_HUMAN ALDLSLK_380.2_185.1 0.016080436 ADA12_HUMAN FGFGGSTDSGPIR_649.3_946.5 0.017037089 B2MG_HUMAN VEHSDLSFSK_383.5_234.1 0.017072093 CO6_HUMAN ALNHLPLEYNSALYSR_621.0_696.4 0.024592775 TRFL_HUMAN YYGYTGAFR_549.3_450.3 0.030890831 AFAM_HUMAN DADPDTFFAK_563.8_302.1 0.033791429 CO6_HUMAN ALNHLPLEYNSALYSR_621.0_538.3 0.034865341 AFAM_HUMAN LPNNVLQEK_527.8_844.5 0.039880594 PEDF_HUMAN TVQAVLTVPK_528.3_428.3 0.040854402 PLMN_HUMAN EAQLPVIENK_570.8_329.2 0.041023812 LBP_HUMAN ITLPDFTGDLR_624.3_920.5 0.042276813 CO8G_HUMAN VQEAHLTEDQIFYFPK_655.7_701.4 0.042353851 PLMN_HUMAN YEFLNGR_449.7_606.3 0.04416504 B2MG_HUMAN VNHVTLSQPK_374.9_459.3 0.045458409 CFAB_HUMAN DISEVVTPR_508.3_472.3 0.046493405 INHBE_HUMAN ALVLELAK_428.8_331.2 0.04789353

TABLE 62 Random Forest Importance Values Using Adjusted Peak Areas Transition Rank Importance INHBE_ALVLELAK_428.8_672.4 1 2964.951571 EGLN_TQILEWAAER_608.8_761.4 2 1218.3406 FA7_SEPRPGVLLR_375.2_654.4 3 998.92897 CBG_ITQDAQLK_458.8_702.4 4 930.9931102 ITIH3_ALDLSLK_380.2_185.1 5 869.6315408 ENPP2_WWGGQPLWITATK_772.4_929.5 6 768.9182114 CBG_ITQDAQLK_458.8_803.4 7 767.8940452 PSG1_LSETNR_360.2_519.3 8 714.6160065 CAA60698_LEPLYSASGPGLRPLVIK_637.4_834.5 9 713.4086612 INHBC_LDFHFSSDR_375.2_611.3 11 681.2442909 CBG_QINSYVK_426.2_610.3 12 674.3363415 LBP_GLQYAAQEGLLALQSELLR_1037.1_858.5 13 603.197751 A1BG_LLELTGPK_435.8_644.4 14 600.9902818 CO6_DLHLSDVFLK_396.2_366.2 15 598.8214342 VCAM1_TQIDSPLSGK_523.3_816.5 16 597.4038769 LRP1_NAVVQGLEQPHGLVVHPLR_688.4_285.2 17 532.0500081 CBG_QINSYVK_426.2_496.3 18 516.5575201 CO6_ENPAVIDFELAPIVDLVR_670.7_811.5 19 501.4669261 ADA12_FGFGGSTDSGPIR_649.3_745.4 20 473.5510333 CO6_DLHLSDVFLK_396.2_260.2 21 470.5473702 ENPP2_TYLHTYESEI_628.3_908.4 22 444.7580726 A1BG_LLELTGPK_435.8_227.2 23 444.696292 FRIH_QNYHQDSEAAINR_515.9_544.3 24 439.2648872 ENPP2_TEFLSNYLTNVDDITLVPGTLGR_846.8_600.3 25 389.3769604 CBG_WSAGLTSSQVDLYIPK_883.0_515.3 26 374.0749768 C1QC_FQSVFTVTR_542.8_623.4 27 370.6957977 GELS_DPDQTDGLGLSYLSSHIANVER_796.4_456.2 28 353.1176588 A1BG_ATWSGAVLAGR_544.8_643.4 29 337.4580124 APOA1_AKPALEDLR_506.8_813.5 30 333.5742035 ENPP2_TYLHTYESEI_628.3_515.3 31 322.6339162 PEPD_AVYEAVLR_460.8_750.4 32 321.4377907 TIMP1_GFQALGDAADIR_617.3_717.4 33 310.0997949 ADA12_LIEIANHVDK_384.6_498.3 34 305.8803542 PGRP2_WGAAPYR_410.7_577.3 35 303.5539874 PSG9_LFIPQITR_494.3_614.4 36 300.7877317 HABP2_FLNWIK_410.7_560.3 37 298.3363186 CBG_WSAGLTSSQVDLYIPK_883.0_357.2 38 297.2474385 PSG2_IHPSYTNYR_384.2_452.2 39 292.6203405 PSG5_LSIPQITTK_500.8_800.5 40 290.2023364 HABP2_FLNWIK_410.7_561.3 41 289.5092933 CO6_SEYGAALAWEK_612.8_788.4 42 287.7634114 ADA12_LIEIANHVDK_384.6_683.4 43 286.5047372 EGLN_TQILEWAAER_608.8_632.3 44 284.5138846 CO6_ENPAVIDFELAPIVDLVR_670.7_601.4 45 273.5146272 FA7_FSLVSGWGQLLDR_493.3_447.3 46 271.7850098 ITIH3_ALDLSLK_380.2_575.3 47 269.9425709 ADA12_FGFGGSTDSGPIR_649.3_946.5 48 264.5698225 FETUA_AALAAFNAQNNGSNFQLEEISR_789.1_746.4 49 247.4728828 FBLN1_AITPPHPASQANIIFDITEGNLR_825.8_459.3 50 246.572102 TSP1_FVFGTTPEDILR_697.9_843.5 51 245.0459575 VCAM1_NTVISVNPSTK_580.3_732.4 52 240.576729 ENPP2_TEFLSNYLTNVDDITLVPGTLGR_846.8_699.4 53 240.1949512 FBLN3_ELPQSIVYK_538.8_409.2 55 233.6825304 ACTB_VAPEEHPVLLTEAPLNPK_652.0_892.5 56 226.9772749 TSP1_FVFGTTPEDILR_697.9_742.4 57 224.4627393 PLMN_EAQLPVIENK_570.8_699.4 58 221.4663735 C1S_IIGGSDADIK_494.8_260.2 59 218.069476 IL1A_ANDQYLTAAALHNLDEAVK_686.4_317.2 60 216.5531949 PGRP2_WGAAPYR_410.7_634.3 61 211.0918302 PSG5_LSIPQITTK_500.8_687.4 62 208.7871461 PSG6_SNPVTLNVLYGPDLPR_585.7_654.4 63 207.9294937 PRG2_WNFAYWAAHQPWSR_607.3_545.3 64 202.9494031 CXCL2_CQCLQTLQGIHLK_13p8RT_533.6_567.4 65 202.9051326 CXCL2_CQCLQTLQGIHLK_13p48RT_533.6_695.4 66 202.6561548 G6PE_LLDFEFSSGR_585.8_553.3 67 201.004611 GELS_TASDFITK_441.7_710.4 68 200.2704809 B2MG_VEHSDLSFSK_383.5_468.2 69 199.880987 CO8B_IPGIFELGISSQSDR_809.9_849.4 70 198.7563875 PSG8_LQLSETNR_480.8_606.3 71 197.6739966 LBP_GLQYAAQEGLLALQSELLR_1037.1_929.5 72 197.4094851 AFAM_LPNNVLQEK_527.8_844.5 73 196.8123228 MAGE 74 196.2410502 PSG2_IHPSYTNYR_384.2_338.2 75 196.2410458 PSG9_LFIPQITR_494.3_727.4 76 193.5329266 TFR1_YNSQLLSFVR_613.8_734.5 77 193.2711994 C1R_QRPPDLDTSSNAVDLLFFTDESGDSR_961.5_866.3 78 193.0625419 PGH1_ILPSVPK_377.2_264.2 79 190.0504508 FA7_SEPRPGVLLR_375.2_454.3 80 188.2718422 FA7_TLAFVR_353.7_274.2 81 187.6895294 PGRP2_DGSPDVTTADIGANTPDATK_973.5_844.4 82 185.6017519 C1S_IIGGSDADIK_494.8_762.4 83 184.5985543 PEPD_VPLALFALNR_557.3_620.4 84 184.3962957 C1S_EDTPNSVWEPAK_686.8_630.3 85 179.2043504 CHL1_TAVTANLDIR_537.3_802.4 86 174.9866792 CHL1_VIAVNEVGR_478.8_744.4 88 172.2053147 SDF1_ILNTPNCALQIVAR_791.9_341.2 89 171.4604557 PAI1_EVPLSALTNILSAQLISHWK_740.8_996.6 90 169.5635635 AMBP_AFIQLWAFDAVK_704.9_650.4 91 169.2124477 G6PE_LLDFEFSSGR_585.8_944.4 92 168.2398598 THBG_SILFLGK_389.2_577.4 93 166.3110206 PRDX2_GLFIIDGK_431.8_545.3 94 164.3125132 ENPP2_WWGGQPLWITATK_772.4_373.2 95 163.4011689 VGFR3_SGVDLADSNQK_567.3_662.3 96 162.8822352 C1S_EDTPNSVWEPAK_686.8_315.2 97 161.6140915 AFAM_DADPDTFFAK_563.8_302.1 98 159.5917449 CBG_SETEIHQGFQHLHQLFAK_717.4_447.2 99 156.1357404 C1S_LLEVPEGR_456.8_686.4 100 155.1763293 PTGDS_GPGEDFR_389.2_623.3 101 154.9205208 ITIH2_IYLQPGR_423.7_329.2 102 154.6552717 FA7_TLAFVR_353.7_492.3 103 152.5009422 FA7_FSLVSGWGQLLDR_493.3_403.2 104 151.9971204 SAMP_VGEYSLYIGR_578.8_871.5 105 151.4738449 APOH_EHSSLAFWK_552.8_267.1 106 151.0052645 PGRP2_AGLLRPDYALLGHR_518.0_595.4 107 150.4149907 C1QC_FNAVLTNPQGDYDTSTGK_964.5_333.2 108 149.2592827 PGRP2_AGLLRPDYALLGHR_518.0_369.2 109 147.3609354 PGRP2_TFTLLDPK_467.8_686.4 111 145.2145223 CO5_TDAPDLPEENQAR_728.3_843.4 112 144.5213118 THRB_ELLESYIDGR_597.8_839.4 113 143.924639 GELS_DPDQTDGLGLSYLSSHIANVER_796.4_328.1 114 142.8936101 TRFL_YYGYTGAFR_549.3_450.3 115 142.8651352 HEMO_QGHNSVFLIK_381.6_260.2 116 142.703845 C1S_GDSGGAFAVQDPNDK_739.3_716.3 117 142.2799122 B1A4H9_AHQLAIDTYQEFR_531.3_450.3 118 138.196407 C1S_SSNNPHSPIVEEFQVPYNK_729.4_261.2 119 136.7868935 HYOU1_LPATEKPVLLSK_432.6_347.2 120 136.1146437 FETA_GYQELLEK_490.3_502.3 121 135.2890322 LRP1_SERPPIFEIR_415.2_288.2 122 134.6569527 CO6_SEYGAALAWEK_612.8_845.5 124 132.8634704 CERU_TTIEKPVWLGFLGPIIK_638.0_844.5 125 132.1047746 IBP1_AQETSGEEISK_589.8_850.4 126 130.934446 SHBG_VVLSSGSGPGLDLPLVLGLPLQLK_791.5_768.5 127 128.2052287 CBG_SETEIHQGFQHLHQLFAK_717.4_318.1 128 127.9873837 A1AT_LSITGTYDLK_555.8_696.4 129 127.658818 PGRP2_DGSPDVTTADIGANTPDATK_973.5_531.3 130 126.5775806 C1QB_LEQGENVFLQATDK_796.4_675.4 131 126.1762726 EGLN_GPITSAAELNDPQSILLR_632.4_826.5 132 125.7658253 IL12B_YENYTSSFFIR_713.8_293.1 133 125.0476631 B2MG_VEHSDLSFSK_383.5_234.1 134 124.9154706 PGH1_AEHPTWGDEQLFQTTR_639.3_765.4 135 124.8913193 INHBE_ALVLELAK_428.8_331.2 136 124.0109276 HYOU1_LPATEKPVLLSK_432.6_460.3 137 123.1900369 CXCL2_CQCLQTLQGIHLK_13p48RT_533.6_567.4 138 122.8800873 PZP_AVGYLITGYQR_620.8_523.3 139 122.4733204 AFAM_IAPQLSTEELVSLGEK_857.5_333.2 140 122.4707849 ICAM1_VELAPLPSWQPVGK_760.9_400.3 141 121.5494206 CHL1_VIAVNEVGR_478.8_284.2 142 119.0877137 APOB_ITENDIQIALDDAK_779.9_632.3 143 118.0222045 SAMP_AYSDLSR_406.2_577.3 144 116.409429 AMBP_AFIQLWAFDAVK_704.9_836.4 145 116.1900846 EGLN_GPITSAAELNDPQSILLR_632.4_601.4 146 115.8438804 LRP1_NAVVQGLEQPHGLVVHPLR_688.4_890.6 147 114.539707 SHBG_VVLSSGSGPGLDLPLVLGLPLQLK_791.5_598.4 148 113.1931134 IBP1_AQETSGEEISK_589.8_979.5 149 112.9902709 PSG6_SNPVTLNVLYGPDLPR_585.7_817.4 150 112.7910917 APOC3_DYWSTVK_449.7_347.2 151 112.544736 C1R_WILTAAHTLYPK_471.9_621.4 152 112.2199708 ANGT_ADSQAQLLLSTVVGVFTAPGLHLK_822.5_983.6 153 111.9634671 PSG9_DVLLLVHNLPQNLPGYFWYK_810.4_328.2 154 111.5743214 A1AT_AVLTIDEK_444.8_605.3 155 111.216651 PSGx_ILILPSVTR_506.3_785.5 156 110.8482935 THRB_ELLESYIDGR_597.8_710.4 157 110.7496103 SHBG_ALALPPLGLAPLLNLWAKPQGR_770.5_256.2 158 110.5091269 PZP_QTLSWTVTPK_580.8_545.3 159 110.4675104 SHBG_ALALPPLGLAPLLNLWAKPQGR_770.5_457.3 160 110.089808 PSG4_TLFIFGVTK_513.3_811.5 161 109.9039967 PLMN_YEFLNGR_449.7_293.1 162 109.6880397 PEPD_AVYEAVLR_460.8_587.4 163 109.3697285 PLMN_LSSPAVITDK_515.8_830.5 164 108.963353 FINC_SYTITGLQPGTDYK_772.4_352.2 165 108.452612 C1R_WILTAAHTLYPK_471.9_407.2 166 107.8348417 CHL1_TAVTANLDIR_537.3_288.2 167 107.7278897 TENA_AVDIPGLEAATPYR_736.9_286.1 168 107.6166195 CRP_YEVQGEVFTKPQLWP_911.0_293.1 169 106.9739589 APOB_SVSLPSLDPASAK_636.4_885.5 170 106.5901668 PRDX2_SVDEALR_395.2_488.3 171 106.2325046 CO8A_YHFEALADTGISSEFYDNANDLLSK_940.8_301.1 172 105.8963287 C1QC_FQSVFTVTR_542.8_722.4 173 105.4338742 PSGx_ILILPSVTR_506.3_559.3 174 105.1942655 VCAM1_TQIDSPLSGK_523.3_703.4 175 105.0091767 VCAM1_NTVISVNPSTK_580.3_845.5 176 104.8754444 CSH_ISLLLIESWLEPVR_834.5_500.3 177 104.6158295 HGFA_EALVPLVADHK_397.9_439.8 178 104.3383142 CGB1_CRPINATLAVEK_457.9_660.4 179 104.3378072 APOB_IEGNLIFDPNNYLPK_874.0_414.2 180 103.9849346 C1QB_LEQGENVFLQATDK_796.4_822.4 181 103.9153207 APOH_EHSSLAFWK_552.8_838.4 182 103.9052103 CO5_LQGTLPVEAR_542.3_842.5 183 103.1061869 SHBG_IALGGLLFPASNLR_481.3_412.3 184 102.2490294 B2MG_VNHVTLSQPK_374.9_459.3 185 102.1204362 APOA2_SPELQAEAK_486.8_659.4 186 101.9166647 FLNA_TGVAVNKPAEFTVDAK_549.6_258.1 187 101.5207852 PLMN_YEFLNGR_449.7_606.3 188 101.2531011

TABLE 63 Random Forest Importance Values Using Peak Area Ratios Variable Rank Importance HABP2_FLNWIK_410.7_561.3 1 3501.905733 ADA12_FGFGGSTDSGPIR_649.3_946.5 2 3136.589992 A1BG_LLELTGPK_435.8_227.2 3 2387.891934 B2MG_VEHSDLSFSK_383.5_234.1 4 1431.31771 ADA12_FGFGGSTDSGPIR_649.3_745.4 5 1400.917331 B2MG_VEHSDLSFSK_383.5_468.2 6 1374.453629 APOB_IEGNLIFDPNNYLPK_874.0_414.2 7 1357.812445 PSG9_DVLLLVHNLPQNLPGYFWYK_810.4_960.5 8 1291.934596 A1BG_LLELTGPK_435.8_644.4 9 1138.712941 ITIH3_ALDLSLK_380.2_185.1 10 1137.127027 ENPP2_TYLHTYESEI_628.3_908.4 11 1041.036693 IL12B_YENYTSSFFIR_713.8_293.1 12 970.1662913 ENPP2_WWGGQPLWITATK_772.4_373.2 13 953.0631062 ENPP2_TYLHTYESEI_628.3_515.3 14 927.3512901 PSG9_LFIPQITR_494.3_614.4 15 813.9965357 MAGE 16 742.2425022 ENPP2_WWGGQPLWITATK_772.4_929.5 17 731.5206413 CERU_TTIEKPVWLGFLGPIIK_638.0_640.4 18 724.7745695 ITIH3_ALDLSLK_380.2_575.3 19 710.1982467 PSG2_IHPSYTNYR_384.2_452.2 20 697.4750893 ITIH1_LWAYLTIQELLAK_781.5_371.2 21 644.7416886 INHBE_ALVLELAK_428.8_331.2 22 643.008853 HGFA_LHKPGVYTR_357.5_692.4 23 630.8698445 TRFL_YYGYTGAFR_549.3_450.3 24 609.5866675 THBG_AVLHIGEK_289.5_348.7 25 573.9320948 GELS_TASDFITK_441.7_710.4 26 564.3288862 PSG9_LFIPQITR_494.3_727.4 27 564.1749327 VGFR3_SGVDLADSNQK_567.3_662.3 28 563.8087791 INHA_TTSDGGYSFK_531.7_860.4 29 554.210214 PSG9_DVLLLVHNLPQNLPGYFWYK_810.4_328.2 30 545.1743627 HYOU1_LPATEKPVLLSK_432.6_347.2 31 541.6208032 CO8G_VQEAHLTEDQIFYFPK_655.7_701.4 32 541.3193428 BMI 33 540.5028818 HGFA_LHKPGVYTR_357.5_479.3 34 536.6051948 PSG2_IHPSYTNYR_384.2_338.2 35 536.5363489 GELS_AQPVQVAEGSEPDGFWEALGGK_758.0_623.4 36 536.524931 PSG6_SNPVTLNVLYGPDLPR_585.7_654.4 37 520.108646 HABP2_FLNWIK_410.7_560.3 38 509.0707814 PGH1_ILPSVPK_377.2_527.3 39 503.593718 HYOU1_LPATEKPVLLSK_432.6_460.3 40 484.047422 CO6_ALNHLPLEYNSALYSR_621.0_696.4 41 477.8773179 INHBE_ALVLELAK_428.8_672.4 42 459.1998276 PLMN_LSSPAVITDK_515.8_743.4 43 452.9466414 PSG9_DVLLLVHNLPQNLPGYFWYK_810.4_215.1 44 431.8528248 BGH3_LTLLAPLNSVFK_658.4_875.5 45 424.2540315 AFAM_LPNNVLQEK_527.8_730.4 46 421.4953221 ITIH2_LSNENHGIAQR_413.5_519.8 47 413.1231437 GELS_TASDFITK_441.7_781.4 48 404.2679723 FETUA_AHYDLR_387.7_566.3 49 400.4711207 CERU_TTIEKPVWLGFLGPIIK_638.0_844.5 50 396.2873451 PSGx_ILILPSVTR_506.3_785.5 51 374.5672526 APOB_SVSLPSLDPASAK_636.4_885.5 52 371.1416438 FLNA_TGVAVNKPAEFTVDAK_549.6_258.1 53 370.4175588 PLMN_YEFLNGR_449.7_606.3 54 367.2768078 PSGx_ILILPSVTR_506.3_559.3 55 365.7704321

From the foregoing description, it will be apparent that variations and modifications can be made to the invention described herein to adopt it to various usages and conditions. Such embodiments are also within the scope of the following claims.

The recitation of a listing of elements in any definition of a variable herein includes definitions of that variable as any single element or combination (or subcombination) of listed elements. The recitation of an embodiment herein includes that embodiment as any single embodiment or in combination with any other embodiments or portions thereof.

All patents and publications mentioned in this specification are herein incorporated by reference to the same extent as if each independent patent and publication was specifically and individually indicated to be incorporated by reference. 

1-6. (canceled)
 7. A method of determining probability for preterm birth in a pregnant female, the method comprising detecting a measurable feature of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63 in a biological sample obtained from said pregnant female, and analyzing said measurable feature to determine the probability for preterm birth in said pregnant female.
 8. The method of claim 7, wherein said measurable feature comprises fragments or derivatives of each of said N biomarkers selected from the biomarkers listed in Tables 1 through
 63. 9. The method of claim 7, wherein said detecting a measurable feature comprises quantifying an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63, combinations or portions and/or derivatives thereof in a biological sample obtained from said pregnant female.
 10. The method of claim 9, further comprising calculating the probability for preterm birth in said pregnant female based on said quantified amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through
 63. 11. The method of claim 7, further comprising an initial step of providing a biomarker panel comprising N of the biomarkers listed in Tables 1 through
 63. 12. The method of claim 7, further comprising an initial step of providing a biological sample from the pregnant female.
 13. The method of claim 7, further comprising communicating said probability to a health care provider.
 14. The method of claim 13, wherein said communication informs a subsequent treatment decision for said pregnant female.
 15. The method of claim 7, wherein N is a number selected from the group consisting of 2 to
 24. 16. The method of claim 15, wherein said N biomarkers comprise at least two of the isolated biomarkers selected from the group consisting of AFTECCVVASQLR, ELLESYIDGR, ITLPDFTGDLR, the biomarkers set forth in Table 50, and the biomarkers set forth in Table
 52. 17-23. (canceled)
 24. The method of claim 7, wherein said quantifying comprises mass spectrometry (MS).
 25. The method of claim 24, wherein said MS comprises liquid chromatography-mass spectrometry (LC-MS).
 26. The method of claim 24, wherein said MS comprises multiple reaction monitoring (MRM) or selected reaction monitoring (SRM).
 27. The method of claim 26, wherein said MRM (or SRM) comprises scheduled MRM (SRM).
 28. The method of claim 7, wherein said quantifying comprises an assay that utilizes a capture agent. 29-65. (canceled)
 66. A method of predicting GAB, the method comprising: (a) quantifying in a biological sample obtained from said pregnant female an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63; (b) multiplying and/or thresholding said amount by a predetermined coefficient, (c) determining the predicted GAB birth in said pregnant female comprising adding said individual products to obtain a total risk score that corresponds to said predicted GAB.
 67. A method of predicting time to birth in a pregnant female, the method comprising: (a) obtaining a biological sample from said pregnant female; (b) quantifying an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63 in said biological sample; (c) multiplying and/or thresholding said amount by a predetermined coefficient, (d) determining predicted GAB in said pregnant female comprising adding said individual products to obtain a total risk score that corresponds to said predicted GAB; and (e) subtracting the estimated GA at time biological sample was obtained from the predicted GAB to predict time to birth in said pregnant female. 68-79. (canceled) 